JP5302701B2 - Tire shape inspection method, tire shape inspection device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To quickly perform appropriate shape defect inspections by speedily executing processing for reliably removing measurement values of formation ranges of embossed marks without any mistakes according to distribution information of surface height measurement values of sidewall surfaces of tires. <P>SOLUTION: A processor automatically detects the positions of the embossed marks based on sample surface shape information obtained from samples of tires, and automatically sets coordinates information for a mask range surrounding the area where the marks are present (S2-S15). The processor also superposes a surface shape image based on the sample surface shape information and a mask range image based on the coordinates information for the mask range for displaying on a display means, and changes the coordinates information for the mask range according to operation input side by side with the display (S16). The processor also corrects deviation in the coordinates system between the surface height distribution information obtained from the tires to be inspected and the coordinates information for the mask range after the change and excludes measurement values in the mask range from targets of shape defect inspection processing. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to a tire shape inspection method and apparatus for inspecting a shape defect of a sidewall surface of a tire on which uneven marks are formed.

A tire has a structure in which various materials such as rubber, chemical fiber, and steel cord are laminated. If there is an uneven part in the laminated structure, the part with relatively weak pressure resistance when filled with air. In this case, a raised portion (convex portion) called a bulge and a recessed portion (concave portion) called a dent or a depression are generated. Tires with such shape defects as bulges and dents need to be inspected and excluded from shipment due to safety issues or poor appearance.
Normally, in the tire shape inspection, first, the tire is rotationally driven by a rotating machine in a state where a predetermined displacement sensor is disposed facing the tire surface (sidewall surface or tread surface).

  For example, in Patent Document 1, the surface of a rotating tire is irradiated with slit light (line light), an image of the slit light is captured, and the shape is detected by a light cutting method based on the captured image. A technique for detecting the height of the surface is shown. Thereby, the distribution information of the surface height measurement value is obtained for a large number of positions over the range of 360 ° in the circumferential direction of the tire surface. The information obtained in this way is based on the first coordinate axis (for example, the X axis) that represents the radial direction of the tire and the surface height measurement value at each position over the range of 360 ° in the circumferential direction of the sidewall surface and the tread surface. This is information (hereinafter referred to as surface height distribution information) in which the surface height measurement values are arranged in a two-dimensional coordinate system including a second coordinate axis (for example, the Y axis) representing a direction. Therefore, if it is considered that the surface height measurement value corresponds to the luminance value of each pixel in the image data, the surface height distribution information can be handled in the same way as monochrome image data on a computer (image processing apparatus). .

Further, in the tire shape inspection, the sidewall surface shape defect inspection processing is executed based on the surface height distribution information.
By the way, an uneven mark (hereinafter referred to as a display mark) for displaying a product type and size, a manufacturer's logo, and the like is formed on the sidewall surface of the tire. For this reason, in the shape defect inspection process on the sidewall surface, it is necessary to prevent the unevenness of the display mark from being erroneously detected as a shape defect.
In the conventional shape defect inspection processing, the data obtained by subjecting the measurement value of one line in the circumferential direction of the tire in the surface height distribution information (one line in the first coordinate axis direction) to low-pass filtering is used. Based on this, it is often determined whether or not the change in the tire circumferential direction is within an allowable range.

For example, in paragraph 0003 of Patent Document 3, a high-frequency component is removed from data obtained by performing fast Fourier transform processing on a measurement value for one line in the tire circumferential direction, and the inverse Fourier is further applied to the remaining data. An example in which the low-pass filter process is realized by performing a conversion process is shown.
Further, Patent Document 2 calculates a contact point between a measured value for one line in the circumferential direction of a tire and a parabola, and linearly interpolates between two points of the calculated contact point, thereby reducing a low-pass instead of a fast Fourier transform process. It is shown about implementing the filtering process.
Patent Document 3 discloses a range from data obtained by performing a smooth differential process on a measurement value for one line in the circumferential direction of a tire, from a steep rise position to a steep fall position of the measurement value change. Is detected as a range where the display mark exists, and the range is excluded from the inspection target.
In the conventional technology described above, the change in the surface height of the portion of the shape defect to be detected is relatively gradual in the circumferential direction of the tire, whereas the change in the surface height of the portion of the display mark is steep. It is assumed that.

Japanese Patent Laid-Open No. 11-138654 JP-A-5-215530 JP 2004-156919 A

FIG. 8 is a diagram schematically illustrating an example of the display mark M on the sidewall surface of the tire in a coordinate system in which the radial direction and the circumferential direction of the tire are the X axis and the Y axis, respectively.
As shown in FIG. 8, the display mark M raised from the tire surface has an edge portion extending in the tire circumferential direction (Y-axis direction) and an edge portion extending in an acute angle with respect to the tire circumferential direction. Often doing. For this reason, the surface height measurement value for one line in the circumferential direction of the tire may include a measurement value around the edge portion of the display mark, like the measurement value on the wavy line La in FIG. In the measured value of the surface height on the wavy line La in FIG. 8, the change of the value due to the display mark M is relatively gradual.
Therefore, in the conventional shape defect inspection process in which low-pass filter processing and smooth differential processing are performed on the surface height measurement value for one circumferential line of the tire, the change in the measurement value caused by the shape defect and the display mark are caused. There is a problem that it is difficult to clearly distinguish the change in the measured value. As a result, the display mark portion may be erroneously detected as a shape defect portion, or the shape defect portion may be erroneously recognized as the display mark portion, resulting in a shape defect detection failure.

Further, the surface height measurement value for one line in the circumferential direction of the tire is a plurality of raised portions Mt in a series of the display marks M isolated from others, like the measurement values on the wavy line Lb in FIG. In some cases, the measured value of the non-protruding portion Mb is included. Here, it can be said that the non-protruding portion Mb is a hollow portion within the display range of the display mark M.
The non-protruding portions Mb positioned inside the outline of the series of display marks M tend to have a large height change regardless of shape defects (bulges and dents), and are therefore excluded from the shape defect inspection target. It is desirable.
However, in the conventional shape defect inspection process, the measurement values of the non-lifted portions located within the outline of the display mark are included in the shape defect inspection target, so that the non-lifted portions are the shape defect portions. There was also a problem that could be misdetected.

On the other hand, the position of the display mark M in the coordinate system is automatically detected by the processor based on the surface height distribution information, and the area surrounding the display mark M is automatically excluded as an area to be excluded from the shape defect inspection target. It is possible to set.
However, the processing for automatically detecting the position of the display mark M based on the surface height distribution information is computationally expensive. Therefore, automatically setting the area surrounding the display mark M by the processor for each tire to be inspected has a problem in that it significantly hinders time reduction in the tire inspection process. Furthermore, there is a problem that it is very difficult to automatically set the region surrounding the display mark M without exception for all of the various types of tires having various surface shapes.
It is also conceivable that a specific region corresponding to the position of the display mark M in the coordinate system of the surface height distribution information is registered in advance as a region to be excluded from the shape defect inspection target.
However, for each of a large number of the display marks M existing on the sidewall surface of the tire, it is very troublesome to set a large number of areas to be excluded from the shape defect inspection target while confirming their positions. There was a problem that there was.
Accordingly, the present invention has been made in view of the above circumstances, and the object of the present invention is to inspect a shape defect on a sidewall surface of a tire on which uneven marks (the display marks) are formed. Tire shape inspection that can perform correct shape defect inspection in a short time by executing processing that removes the measured value in the range where the mark is formed from the surface height measurement value reliably and without misunderstanding. It is to provide a method and apparatus thereof.

In order to achieve the above object, a tire shape inspection method according to the present invention is based on surface height distribution information about a sidewall surface of a tire on which concave and convex marks are formed. This is a method for executing the steps shown in the following (1-1) to (1-6). Here, the surface height distribution information indicates that the surface height measurement value at each position over the entire circumferential range of the sidewall surface measured by scanning the sidewall surface in the circumferential direction indicates the radial direction of the tire. This is information arranged in a two-dimensional coordinate system composed of a first coordinate axis that represents and a second coordinate axis that represents the circumferential direction of the tire.
(1-1) Automatically detecting the position of the uneven mark based on the sample surface shape information which is the surface height distribution information obtained from the tire sample determined for each type of the tire to be inspected. , Mask range automatic setting step of automatically setting the coordinate information of the mask range surrounding the existence range of the uneven mark.
(1-2) An image output step of superimposing a surface shape image based on the sample surface shape information and a mask range image based on the coordinate information of the mask range on a display unit.
(1-3) A mask range changing step of changing the coordinate information of the mask range in accordance with an operation input through a predetermined operation unit in parallel with the image output step.
(1-4) An information registration step of storing in the storage means the coordinate information of the mask range after the change by the mask range change step and the registered surface shape information which is a part or all of the sample surface shape information.
(1-5) Coordinates of the inspection surface shape information and the mask range by collating the inspection surface shape information which is the surface height distribution information obtained for each tire to be inspected and the registered surface shape information A coordinate system deviation detection step of detecting a deviation of the coordinate system between the information.
(1-6) Processing for excluding the surface height measurement value in the range corresponding to the coordinate information of the mask range in the inspection surface shape information from the object of shape defect inspection processing after correcting the coordinate system deviation Mask range inspection exclusion process.
The above six steps are usually executed by a predetermined processor. In addition to the case where each of the above six steps is executed by an individual processor, it is also conceivable that one processor executes a plurality of steps together. Of course, in carrying out the tire shape inspection method according to the present invention, the number of processors used and how the six processes are shared and executed by these processors are not particularly limited.

In the tire shape inspection method according to the present invention, the step (1-1) includes a process of automatically detecting the position of the concave and convex marks, and therefore is a step with a relatively high calculation load by the processor. According to the present invention, the steps (1-1) to (1-4) including processing with a high calculation load may be executed for one sample tire for each type of tire.
On the other hand, the processes in the steps (1-5) and (1-6) executed for each tire to be inspected are processes with a relatively low calculation load by the processor. Can be executed. Therefore, the tire shape inspection method according to the present invention does not hinder the time reduction of the inspection process for each tire to be inspected.
In addition, it is very difficult to automatically set the mask range that surrounds the uneven mark without exception for all types of tires having various surface shapes by executing the mask range automatic setting step. On the other hand, by executing the mask range changing step, it is possible to manually correct the mask range while visually confirming the mask range automatically set on the surface shape image of the sidewall surface. It is. Therefore, the correct mask range can be reliably set for many types of tires having various surface shapes. In addition, the manual operation in the mask range changing process only needs to be performed on a part of the mask range that is set automatically. Therefore, the manual operation in the mask range changing step is much more difficult than the manual setting of the mask range one by one for all of the many uneven marks present on the sidewall surface. It is a simple task.

By the way, the measured value of the surface height in the surface height distribution information is obtained by scanning the sidewall surface of the tire while rotating the tire set on a predetermined rotation axis. At that time, the start position of scanning on the sidewall surface is generally not particularly defined. Further, the tire shape inspection device usually does not have a function of detecting the direction of the tire set on the rotating shaft and recording the detection result. Therefore, in the surface height distribution information obtained by the tire shape inspection device, the correspondence between the coordinates on the coordinate axis corresponding to the circumferential direction of the tire and the actual circumferential position of the tire is determined for each tire to be inspected. It is not unified. Therefore, it is necessary to detect and correct a shift in the coordinate system between the surface height distribution information obtained for each tire to be inspected and the coordinate information of the mask range obtained by the mask range changing step. .
According to the tire shape inspection method of the present invention, in the coordinate system deviation detection step, the deviation of the coordinate system is automatically detected, and the deviation of the coordinate system is corrected according to the detection result. The range to be excluded from the target can be set correctly.

It is also conceivable that the tire shape inspection method according to the present invention satisfies the requirements shown in the following (1-7) and (1-8).
(1-7) The registered surface shape information is the surface height measurement value over the entire direction of the second coordinate axis at a specific coordinate in the direction of the first coordinate axis in the sample surface shape information.
(1-8) The coordinate system deviation detection step includes the surface height measurement value over the entire direction of the second coordinate axis at the specific coordinate in the direction of the first coordinate axis in the surface shape information for inspection, A step of detecting a shift of the coordinate system in the second coordinate axis direction by collating the registered surface shape information while shifting the position in the second coordinate axis direction.
Normally, the coordinate system misalignment in question often occurs only in the direction of the coordinate axis corresponding to the circumferential direction of the tire. Therefore, the misalignment of the coordinate system can be easily detected by the step (1-8). .

As a specific example of the tire shape inspection method according to the present invention, the mask range automatic setting step may include the following steps (1-9) to (1-11).
(1-9) A two-dimensional edge detection step of detecting the edge of the uneven mark by two-dimensional edge detection processing on the sample surface shape information and storing the detected two-dimensional edge distribution information in a storage means.
(1-10) A labeling step in which a labeling process is performed on the two-dimensional edge distribution information, and the label distribution information as a result of the process is stored in a storage unit.
(1-11) A mask range setting step of setting the coordinates of the mask range surrounding the existence range of the uneven marks based on the fillet coordinates for each label value in the label distribution information, and storing the coordinates in the storage means .
Here, in the two-dimensional edge detection step, for example, an edge of the uneven mark is detected by sequentially performing two-dimensional smooth differentiation processing and binarization processing on the surface height distribution information. Then, the binary distribution information as the processing result or the corrected binary distribution information obtained by performing a predetermined correction process on the binary distribution information is detected as the two-dimensional edge distribution information. An example of the two-dimensional smooth differentiation process is a two-dimensional Sobel filter process.
In the two-dimensional edge detection step, the surface height distribution information, which is two-dimensional information, is subjected to edge detection processing with the two-dimensional information as it is, thereby detecting the edge of the display mark (uneven mark). It is a process. Thereby, even if the edge part (outline part) of the display mark extends in any direction in the two-dimensional coordinates, the edge part is reliably detected. Various two-dimensional differentiation processes can be adopted as the two-dimensional edge detection process.
Further, the labeling step sets the same label value for each series of display marks isolated from others, and the mask range setting step sets a rectangular range that surrounds the display marks with a minimum range for each series of display marks. Coordinates (fillet coordinates for each label value) are detected. The labeling process is a process of setting the same label value for each connected pixel with respect to the two-dimensional edge distribution information (binary distribution information) that can be regarded as binary image information.
Then, according to the mask range setting step, based on the fillet coordinates for each of the label values, a mask range including the display mark existing range, that is, a range in which the surface height measurement value is excluded from the shape defect inspection target. Is set. As a result, it is possible to exclude the measurement value of the non-protrusion portion located within the outline of the series of the display marks from the object of the shape defect inspection, and to erroneously detect the non-protrusion portion as the shape defect portion. Can be avoided.

Further, it is conceivable that the image output step includes a step of causing the display means to display a cursor image whose display position moves in response to the operation input.
Further, it is conceivable that the mask range changing step includes a step of changing the coordinate information of the mask range for the coordinates corresponding to the display position of the cursor image. Thereby, the coordinate information of the mask range can be changed by a simple operation on the operation unit such as a mouse.
Further, the mask range inspection exclusion step converts the surface height measurement value in a range corresponding to the coordinate information of the mask range in the surface shape information for inspection into an interpolation value based on the surface height measurement value other than the range. It is conceivable that this is a replacement process. This interpolation value is a slowly changing value such as a linear interpolation value or a quadratic curve interpolation value. Thereby, the shape defect inspection process can be executed with the same algorithm regardless of the presence or absence of the mask range.
Further, as a specific example of the mask range interpolation step, the processor performs a straight line based on the surface height measurement value outside the mask range in the surface height distribution information for each line in the second coordinate axis direction. It is conceivable to calculate an interpolation value of the surface height measurement value within the mask range by interpolation.

Further, the predetermined correction process performed on the binary distribution information may include an expansion process performed in the field of image processing.
As a result, even when a part of the contour of the display mark includes a part where the rise (change) of the surface height is relatively gentle, the part is recognized as a part included in the contour of the display mark, and the label Value assignment is ensured.
In the present invention, in the mask range setting step, each rectangular range specified by the fillet coordinates for each label value may be set as the mask range. However, since the rectangular range specified by the fillet coordinates of the label value can include a range to be subjected to shape defect inspection, it is desirable to set the mask range in more detail.

Therefore, in the present invention, the labeling process satisfies the conditions shown in the following (1-12), and the mask range setting process is a process shown in the following (1-13) and (1-14). It is conceivable to have
(1-12) The labeling step is a result of performing a labeling process on the two-dimensional edge distribution information (binary distribution information) on the assumption that the coordinates of both ends of the entire circumference range are adjacent to each other. It is a step of storing the label distribution information in the storage means.
(1-13) For each of the label values in the label distribution information, any one of the three types of presence patterns in which the label value existence range pattern in the second coordinate axis direction is determined in advance based on the fillet coordinates. A label presence pattern discrimination step of discriminating whether or not and storing the discrimination result in a storage means.
(1-14) For each line in the second coordinate axis direction, the coordinates of the mask range are set according to the discrimination result and position of the existence pattern of each of the label values existing on the one line, A line-by-line mask range setting step for storing coordinates in a storage means.
The three kinds of predetermined existence patterns include a first existence pattern in which the label value continuously exists over the entire circumference range, and one end portion of the circumference range in which the label value exists. The third existence pattern is a second existence pattern that is separated into a region including the other end portion and a region including the other end, and a third existence pattern that is in another state.

More specifically, in the line-by-line mask range setting step, the processor performs the following (1-15) to (1) for each line in the second coordinate axis direction according to the presence pattern discrimination result. It is conceivable to execute the process shown in -17).
(1-15) For the label value whose determination result of the presence pattern is the first presence pattern, whether only the position where the label value exists is set in the mask range according to the number of label values Either one of the processes for setting all the lines in the second coordinate axis direction to the mask range is executed.
(1-16) With respect to the label value whose determination result of the presence pattern is the second presence pattern, in each range that bisects the entire circumference range, from each end position of the entire circumference range to the position A process of setting the range reaching the position of the label value farthest as the mask range is executed.
(1-17) For the label value whose determination result of the presence pattern is the third presence pattern, a process for setting the range over the entire position where the label value exists as the mask range is executed.
According to the processing described above, as will be described later, a necessary minimum range corresponding to a substantially inner side from the outline of the display mark is set as the mask range.

Incidentally, the sidewall surface of the tire is generally curved basically in the first coordinate axis direction (radial direction) regardless of the presence or absence of the display mark. Therefore, when the degree of curvature of the sidewall surface is steep, the curved portion may be erroneously detected as an edge of the display mark in the two-dimensional Sobel filter processing in the filtering step.
Therefore, it is conceivable to execute the following processes (1-18) and (1-19) by the processor.
(1-18) The surface height measurement value in the surface height distribution information is normalized according to an average value of the surface height measurement values for one line for each line in the second coordinate axis direction. A measurement value normalization step is executed.
(1-19) In the two-dimensional edge detection step, a two-dimensional edge detection process is executed on the surface height distribution information in which the surface height measurement value is normalized by the measurement value normalization step.
Thereby, it can avoid that the curved part which is the original shape of the said side wall surface is misdetected as the edge of the said display mark.

The present invention can also be understood as a tire shape inspection apparatus for deriving surface height distribution information of a sidewall surface of a tire used for shape defect inspection based on the tire shape inspection method according to the present invention.
That is, the tire shape inspection apparatus according to the present invention performs irradiation of line light on a sidewall surface where uneven marks are formed on a relatively rotating tire and picks up an image of the line light. Is a device for deriving surface height distribution information used for the inspection of the shape defect of the tire, and includes the following components (2-1) to (2-9).
(2-1) Line light irradiating means for continuously irradiating a plurality of line lights from a direction different from the detection height direction in the light cutting line so that one light cutting line is formed on the sidewall surface.
(2-2) Image pickup means for picking up images of the plurality of line lights irradiated on the sidewall surface in a direction in which the principal rays of the plurality of line lights are regularly reflected with respect to the sidewall surface.
(2-3) Light cutting method shape detection means for deriving surface height distribution information over the entire circumference of the sidewall surface by detecting a light cutting line in a captured image of the imaging means.
(2-4) The position of the uneven mark is automatically detected based on the sample surface shape information that is the surface height distribution information obtained from the tire sample, and the presence range of the uneven mark is surrounded. Mask range automatic setting means for automatically setting the coordinate information of the mask range.
(2-5) Image output means for displaying on the display means a surface shape image based on the sample surface shape information and a mask range image based on the coordinate information of the mask range in an overlapping manner.
(2-6) Mask range changing means for changing the coordinate information of the mask range in accordance with an operation input through a predetermined operation unit in parallel with the processing of the image output means.
(2-7) Information registration means for storing in the storage means the coordinate information of the mask range after being changed by the mask range changing means and the registered surface shape information which is a part or all of the sample surface shape information.
(2-8) Coordinate information of the inspection surface shape information and the mask range by collating the inspection surface shape information which is the surface height distribution information obtained from the tire to be inspected and the registered surface shape information Coordinate system deviation detecting means for detecting a deviation of the coordinate system between
(2-9) Processing for excluding the surface height measurement value in the range corresponding to the coordinate information of the mask range in the inspection surface shape information from the object of shape defect inspection processing after correcting the coordinate system deviation Means for excluding mask range inspection.
The “relatively rotating tire” refers to the case where the tire itself rotates about its rotational axis, and the line light irradiation means and the imaging means in a state where the tire itself is fixed. The case of rotating to the center.
The tire shape inspection apparatus according to the present invention has the same effects as the tire shape inspection method according to the present invention.

  According to the present invention, when inspecting the shape defect of the sidewall surface of the tire on which the uneven mark (the display mark) is formed, the measurement value in the range in which the mark is formed is obtained from the surface height measurement value. It is possible to execute a process for reliably and mistakenly at high speed. As a result, according to the present invention, correct shape defect inspection can be performed in a short time.

The figure showing schematic structure of the tire shape test | inspection apparatus W which concerns on embodiment of this invention. The figure which represented typically the three-dimensional arrangement | positioning of the light source and camera in a sensor unit with which the tire shape inspection apparatus W is provided. The flowchart showing an example of the procedure of the mask range setting process by the tire shape inspection apparatus W. The figure which represented as an image an example of the binary distribution information of the sidewall surface of the tire obtained in the middle of a shape defect inspection. The figure which represented as an image an example of the binary distribution information after correction | amendment of the sidewall surface of the tire obtained in the middle of a shape defect inspection. The figure showing an example of the mask range of the sidewall surface of the tire set in the middle of the shape defect inspection as a binary image. The figure which represented another example of the mask range of the sidewall surface of the tire set in the middle of a shape defect inspection as a binary image. The schematic diagram of the display mark of the sidewall surface of a tire. The flowchart showing an example of the procedure of the shape defect inspection process by the tire shape inspection apparatus W. The 1st example of the display screen by the image output process in the tire shape test | inspection apparatus W. FIG. The 2nd example of the display screen by the image output process in the tire shape test | inspection apparatus W. FIG. The 3rd example of the display screen by the image output process in the tire shape test | inspection apparatus W. FIG. The 4th example of the display screen by the image output process in the tire shape inspection apparatus W.

  Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings for understanding of the present invention. The following embodiment is an example embodying the present invention, and does not limit the technical scope of the present invention.

The tire shape inspection apparatus W according to the embodiment of the present invention captures an image of line light irradiated on the surface of the rotating tire 1 by a camera, and performs shape detection by a light cutting method based on the captured image. A shape measurement process for measuring the surface height distribution of the tire 1 is executed. By this shape measurement process, surface height distribution information representing the distribution of the surface height measurement values at each position over the range of 360 ° in the circumferential direction of the surface of the tire 1 is obtained. The measurement target of the surface height distribution information is the tread surface and the sidewall surface of the tire 1.
Further, the tire shape inspection device W is an inspection surface height which is information obtained by correcting the surface height distribution information obtained by the shape measurement process or a part of the surface height distribution information as necessary. Based on the distribution information, a shape defect inspection process on the surface of the tire 1 is executed.

First, an overall configuration of a tire shape inspection apparatus W according to an embodiment of the present invention will be described with reference to FIG.
As shown in FIG. 1, the tire shape inspection device W includes a tire rotating machine 2, a sensor unit 3, a unit driving device 4, an encoder 5, an image processing device 6, a host computer 7, and the like.
The tire rotating machine 2 is a rotating device such as a motor that rotates the tire 1 that is the object of shape detection around the rotating shaft 1g.
For example, the tire rotating machine 2 rotates the tire 1 at a rotation speed of 60 rpm. As a result, the tire shape inspection device W detects the surface shape of the entire circumference range of the tread surface and the sidewall surface of the tire 1 by the sensor unit 3 to be described later during one second in which the tire 1 is rotated once.
The sensor unit 3 is a unit in which a light source that irradiates line light onto the surface of the rotating tire 1 and a camera that captures an image of the line light on the surface of the tire 1 are incorporated. In the present embodiment, two sensor units 3a and 3c used for measuring the shape of each of the two sidewall surfaces of the tire 1 and one sensor unit 3b used for measuring the shape of the tread surface of the tire 1 are combined. Two sensor units 3 are provided.

FIG. 2 is a diagram schematically showing the arrangement of the devices provided in the sensor unit 3.
As shown in FIG. 2, the sensor unit 3 includes a light projecting device 10 that outputs a plurality of line lights, and a camera 20.
In FIG. 2, the X axis is the direction in contact with the circumference of the tire rotation at the shape detection position of the tire 1, the Z axis is the detection height direction (direction of the surface height to be detected) at the shape detection position of the tire 1, and the Y axis is A direction perpendicular to the X axis and the Z axis is represented.
That is, in the sensor units 3a and 3c used for detecting the shape of the sidewall surface of the tire 1, the Z axis is a coordinate axis indicating the direction of the rotation axis 1g of the tire 1, and the X axis is the radial direction of the tire 1 (the tire 1 is a coordinate axis representing a normal direction with respect to 1 rotation axis 1g.
In the sensor unit 3b used for detecting the shape of the tread surface of the tire 1, the Z axis is a coordinate axis representing the radial direction of the tire 1, and the X axis is a coordinate axis representing the direction of the rotation axis 1g of the tire 1. .
In any of the sensor units 3 a, 3 b, 3 c, the Y axis is a coordinate axis that represents the circumferential direction of the tire 1.
The correspondence relationship between the tire 1 and the coordinate axis can be changed according to the support mode of the camera 20.

The light projecting device 10 includes a plurality of (three in FIG. 2) line light sources 11 to 13, and the plurality of line light sources 11 to 13 cuts one light on one line Ls on the surface of the tire 1. In order to form a line, a plurality of line lights are continuously irradiated from a direction different from the detection height direction (Z-axis direction) in the one line Ls (light cutting line) (end portions of adjacent line lights) Are arranged so as to overlap each other and irradiate one line light as a whole).
The camera 20 includes a camera lens 22 and an image sensor 21 (light receiving unit), and a plurality of line light images v <b> 1 (the one of the ones) irradiated continuously to the surface (tread surface or sidewall surface) of the tire 1. An image of an optical cutting line on the line Ls) is taken.
Therefore, in the sensor units 3a and 3c for the sidewall surface, the light projecting device 10 performs light cutting on one line Ls along the radial direction (Y-axis direction) of the tire 1 on the sidewall surface of the tire 1. In order to form a line (one light cutting line), a plurality of line lights are irradiated from a direction different from the detection height direction (Z-axis direction) in the one line Ls (light cutting line). .

On the other hand, in the sensor unit 3b for the tread surface, the light projecting device 10 has an optical cutting line formed on one line Ls along a direction orthogonal to the circumferential direction of the tire on the tread surface of the tire 1. In addition, a plurality of line lights are continuously irradiated from a direction different from the detected height direction (Z-axis direction) in the one line Ls (light cutting line).
In the present embodiment, the irradiation of three line lights is illustrated for each surface of the tire 1 (for each sensor unit 3). However, by increasing or decreasing the number of the line light sources 11 to 13, It is also conceivable to irradiate two line lights or four or more line lights on each surface of one.
In addition, the light projecting device 10 and the camera 20 are configured so that the principal rays (lights along the center line) of the plurality of line lights output from the line light sources 11 to 13 are output from the tire 1 by a holding mechanism (not illustrated). The field of view of the camera 20 is held so as to be regularly reflected with respect to the surface. Thereby, the camera 20 captures images of the plurality of line lights in the direction in which the principal rays of the plurality of line lights are regularly reflected with respect to the surface of the tire 1 (an example of the imaging unit).
When line light is applied to the surface of a glossy tire, the amount of specularly reflected light is greater than that of scattered reflected light. On the other hand, according to the above configuration, the surface of the tire can be obtained even if the line light image is captured at a sufficiently high imaging rate (for example, 4000 frames or more per second) without increasing the line light intensity. It is possible to obtain a clear image of the line light irradiated on.

On the other hand, the unit driving device 4 (see FIG. 1) is a device that supports each sensor unit 3 movably using a driving device such as a servo motor as a driving source, and positions the position of each sensor unit 3 with respect to the tire 1. . The unit driving device 4 moves each sensor unit 3 before the tire 1 is attached to or detached from the tire rotating machine 2 according to an operation on a predetermined operation unit or a control command from an external device. After a new tire 1 is mounted on the tire rotating machine 2, the sensor unit 3 is positioned at a predetermined inspection position close to the tire 1.
The encoder 5 is a sensor for detecting the rotation angle of the rotating shaft of the tire rotating machine 2, that is, the rotation angle of the tire 1, and the detection signal is used to control the imaging timing of the camera provided in the sensor unit 3. Used for.

The image processing device 6 performs shutter control (control of imaging timing) of the camera provided in the sensor unit 3 based on the detection signal of the encoder 5. For example, the image processing device 6 releases the shutter of the camera every time the encoder 5 detects that the tire 1 rotating at a speed of 60 rpm has rotated by 0.09 ° (= 360 ° / 4000). Control as follows. Thereby, imaging is performed at an imaging rate of 4000 frames per second.
Further, the image processing device 6 inputs an image captured by a camera included in the sensor unit 3, that is, a captured image data of a line light image irradiated on the surface of the tire 1, and based on the captured image. The shape measurement processing by the optical cutting method is executed, and the surface height distribution information (a set of surface height measurement values of the tire 1) as the measurement result is stored in the built-in frame memory.
The image processing device 6 is realized by a DSP (Digital Signal Processor), for example.
In addition, since the shape measurement process by the light cutting method is well known, description is omitted here.

The surface height distribution information on the sidewall surface of the tire 1 includes the first surface height measurement value at each position over a range of 360 ° in the circumferential direction of the sidewall surface representing the radial direction of the tire 1. This is information arranged in a two-dimensional coordinate system composed of coordinate axes (here, the X axis) and a second coordinate axis (here, the Y axis) representing the circumferential direction of the tire 1.
Further, the surface height distribution information on the tread surface of the tire 1 indicates that the surface height measurement value at each position over a range of 360 ° in the circumferential direction of the tread surface indicates a direction parallel to the rotation axis of the tire 1. This is information arranged in a two-dimensional coordinate system composed of an X-axis that represents and a Y-axis that represents the circumferential direction of the tire 1.
Hereinafter, the range occupied by the surface height distribution information in the Y-axis direction (the direction of the second coordinate axis), that is, the Y-axis coordinate range corresponding to 360 ° in the circumferential direction of the tire 1 will be referred to as an all-round range Wy. Called. The coordinates at both ends (the start point coordinate and the end point coordinate in the Y-axis direction) in the entire circumferential range Wy correspond to positions adjacent in the circumferential direction on the surface of the actual tire 1.

If the surface height measurement value is considered to correspond to the luminance value of each pixel in the image data, the surface height distribution information can be handled on the image processing device 6 in the same way as monochrome image data. Therefore, the term “pixel” in the following is described as a term representing the position (coordinate) of each surface height measurement value in the coordinate system composed of the X axis and the Y axis.
In addition, the uneven | corrugated mark (a character, a symbol, a figure, etc.) is formed in the sidewall surface of the tire 1, and the mark is hereafter called the display mark M (refer FIG. 8).
Further, the image processing device 6 determines, for the surface height distribution information on the sidewall surface of the tire 1, a surface height measurement value within a range where the display mark M to be excluded from the shape defect inspection target exists. Surface height distribution information correction processing for replacing with an interpolation value that changes gradually is executed. Information on the sidewall surface obtained by the surface height distribution information correction processing and the surface height distribution information on the tread surface of the tire 1 are transmitted to the host computer 7 as the surface height distribution information for inspection. Is done.

The host computer 7 includes a computer main body 71, an operation unit 72 and a display device 73. The computer main body 71 is a main body of a personal computer or the like provided with a CPU that is a processor for performing various data processing, data storage means such as a hard disk drive, and the like. The operation unit 72 is an operation unit for inputting information such as a keyboard and a mouse. The display device 73 is a display such as a liquid crystal display device or a CRT display for displaying character information or image information.
In the host computer 7, the CPU in the computer main body 71 executes various programs and outputs calculation results by executing a program stored in a memory in advance.
Specifically, the host computer 7 executes a shape defect inspection process based on the inspection surface height distribution information of each surface of the tire 1 acquired from the image processing device 6. In this shape defect inspection process, it is determined whether or not the inspection surface height distribution information of each surface of the tire 1 satisfies a preset allowable condition for each surface of the tire 1, and the determination result is determined in advance. Is displayed on the display unit or output as a predetermined control signal.

Next, an example of a mask range setting process procedure for the surface height distribution information regarding the sidewall surface of the tire, which is executed by the image processing device 6 and the host computer 7, with reference to the flowchart shown in FIG. Will be described. Before the processing shown in FIG. 3 is executed, the shape measurement processing is executed as described above, and the surface height distribution information about the sidewall surface of the sample of the tire 1 is stored in the frame memory of the image processing device 6. It shall be remembered. Note that S1, S2,... Shown below represent identification codes of processing procedures (steps).
The process shown in FIG. 3 is a process executed on the surface height distribution information obtained from a tire sample prepared in advance for each type of tire to be inspected. That is, the processes of steps S1 to S17 shown in FIG. 3 are executed only for one sample tire determined for each type of tire to be inspected.

[Step S1]
First, the image processing device 6 executes a measurement value normalization step (S1) for the surface height distribution information of the sidewall surface. Specifically, the image processing device 6 converts the surface height measurement value in the surface height distribution information to an average value of the surface height measurement values for one line for each line in the Y-axis direction. In response, normalization is performed and the normalized surface height distribution information is stored in a built-in frame memory. The normalized value is, for example, a value obtained by subtracting the average value from each surface height measurement value.
When the surface height distribution information includes a provisional measurement value (for example, zero) for a position where a light cutting line with a predetermined luminance or higher is not detected, the image processing device 6 The average value of the surface height measurement values is calculated excluding the temporary measurement values. Further, the image processing device 6 uses, for each line in the Y-axis direction, the provisional measurement value included in the one line as an interpolated value based on other measurement values, for example, an average value of the surface height measurement values. Replace with
The normalized value obtained by the process of step S1 is surface height information from which a curved component in the radial direction (X-axis direction), which is the original shape of the sidewall surface of the tire 1, is removed. Note that the ideal shape in the radial direction of the sidewall surface assuming that there is no display mark M is set in advance, and a value obtained by subtracting the value of the ideal shape from each of the surface height measurement values is a value after normalization. This is also possible.
The image processing device 6 uses the surface height distribution information obtained by normalizing the surface height measurement value in the measurement value normalization step (S1) for the processes in steps S2 to S15 described below. Are stored in the memory and transmitted to the host computer 7.

[Step S2]
Next, the image processing device 6 performs a two-dimensional Sobel filter process on the surface height distribution information in which the surface height measurement value is normalized in the measurement value normalization step (S1), A filtering step of storing the gradient value distribution information as the processing result in the built-in frame memory is executed (S2).
The Sobel filter processing is predetermined according to the position of each of a predetermined number of pixel groups (normalized surface height measurement values) including a certain pixel of interest and surrounding pixels. This is a process of summing the results of multiplying the coefficients. Further, in the two-dimensional Sobel filter processing, the above-described multiplication of the count and the sum of the multiplication results are performed using two coefficient matrices corresponding to the X-axis direction and the Y-axis direction, respectively. The square root is calculated as the processing result. As a result, a higher processing result is obtained as the gradient of the surface height of the sidewall surface increases. Hereinafter, the processing result of each pixel by the two-dimensional Sobel filter processing is referred to as a gradient value, and the set of gradient values of each pixel in the XY coordinate system is referred to as gradient value distribution information. Since the two-dimensional Sobel filter processing is well known, detailed description thereof is omitted here.
In the filtering step (S2), the coordinates (Y coordinate) of both ends of the entire circumference range Wy are adjacent in the Y-axis direction so that the gradient values can be obtained for pixels near both ends of the entire circumference range Wy. Sobel filter processing is executed on the premise that the coordinates are the same.
In the two-dimensional Sobel filter processing in the tire shape inspection, nine pixel groups consisting of the target pixel and the surrounding eight pixels, or 25 pixel groups consisting of the nine pixel groups and the surrounding 16 pixels. The gradient value of the target pixel is calculated based on the value of.

[Step S3]
Subsequently, the image processing device 6 performs a binarization process on the gradient value distribution information and stores the binary distribution information as a result of the processing in the frame memory (S3). ). By this binarization step, an ON value (for example, 1) is set for pixels whose pixel value (the gradient value) is equal to or greater than a preset threshold value, and an OFF value (for example, 0) is set for other pixels. ) Is set.
By the processing of steps S1 to S3 described above, the edge portion (contour portion) of the display mark M is surely detected regardless of the direction in which the two-dimensional coordinates are extended. Steps S2 and S3 detect the edges of the display mark M having irregularities by two-dimensional edge detection processing (two-dimensional Sobel filter processing and binarization processing) on the surface height distribution information, It is an example of the two-dimensional edge detection process which memorize | stores the detection result (two-dimensional edge distribution information) in the said frame memory.
FIG. 4 is a diagram showing an example of the binary distribution information related to the sidewall surface obtained by the process of step S3 as an image. In FIG. 4, the black portion is the pixel portion of the OFF value (= 0) in the binary distribution information, and the white portion is the pixel portion of the ON value (= 1) in the binary distribution information. That is, the white portion in FIG. 4 is the edge portion of the display mark M.

[Step S4]
Next, the image processing device 6 performs a predetermined correction process on the binary distribution information, and stores the corrected information (corrected binary distribution information) in the frame memory. A correction process is executed (S4).
More specifically, in step S4, the image processing device 6 performs a known expansion process on the binary distribution information. The dilation processing is performed when there is at least one ON value (= 1) in the vicinity of a pixel of interest (for example, the so-called 4-neighborhood or 8-neighborhood) for the binary distribution information that can be regarded as binary image information. , Processing for correcting the value of the target pixel to the ON value (= 1).
As a result, even when a part of the contour of the display mark M includes a part where the rise (change) in the surface height is relatively gentle, that part is recognized as a part of the contour of the display mark M. .
FIG. 5 is a diagram showing, as an image, corrected binary distribution information obtained by performing expansion processing on the binary image information imaged in FIG. In FIG. 5, the black portion is the pixel portion of the OFF value (= 0) in the corrected binary distribution information, and the white portion is the pixel portion of the ON value (= 1) in the corrected binary distribution information. is there. That is, the white portion in FIG. 5 is the edge portion of the display mark M.
As a part of the correction process, a known isolated point removal process is performed before the expansion process so that noise caused by small deposits or small protrusions on the sidewall surface is not enlarged by the expansion process. It is possible to do it.

[Step S5]
Next, the image processing device 6 performs a labeling process on the corrected binary distribution information obtained by the process of step S4, and stores the label distribution information as a result of the processing in the frame memory. Is executed (S5).
The labeling process is a well-known process in which the same label value is assigned to each connected pixel, and the label distribution information includes a label value for each pixel value that is an ON value (= 1) in the corrected binary distribution information. It is set information.
In step S5 as well, as in step S2, the labeling process is executed on the assumption that the coordinates (Y coordinate) at both ends of the entire circumference range Wy are adjacent in the Y-axis direction. . Thereby, due to the start position of the shape measurement process, the connected pixel corresponding to the edge portion of the display mark M is separated (divided) into the start side and the end side of the entire circumference range Wy. However, the same label value is set for these pixels.
It is also conceivable that the binary distribution information correction step in step S4 is omitted, and in step S5, a labeling process is performed on the binary distribution information before correction obtained by the process in step S3.

[Step S6]
Next, the image processing device 6 detects a fillet coordinate of the label value for each label value in the label distribution information obtained by the process of step S5, and stores it in a predetermined built-in memory. Is executed (S6). As is well known, the fillet coordinates are coordinates representing a rectangular range that encloses pixel groups (connected pixels) having the same label value with a minimum range.

[Steps S7 to S14]
Next, the image processing device 6 sets the coordinates of the mask range including the range in which the display mark M exists based on the fillet coordinates of the label value obtained in step S6, and stores the coordinates therein. The mask range setting process to be stored in is executed (S7 to S14). The mask range is a range surrounding the pixel group for each pixel group for which the same label value is set in the label distribution information.
Hereinafter, the contents of the mask range setting process will be described in detail.

[Step S7]
First, for each label value in the label distribution information, the image processing device 6 uses the fillet coordinates of the label value (that is, connected pixels) and the pattern of the range of label values in the Y-axis direction (circumferential direction). Is determined to be one of the three types of presence patterns determined in advance, and a label presence pattern determination step of storing the determination result in the built-in memory is executed (S7).
The three types of presence patterns are the following three patterns P1 to P3. FIG. 6 shows images corresponding to the patterns P1 to P3.
The first is a circulation pattern P1 (corresponding to the first existence pattern) in which a label value exists continuously over the entire circumference range Wy.
Second, a separation pattern P2 (labeled as the second pattern) in which the label value is separated into a region including the start end (one end) and a region including the end (the other end) of the entire circumference range Wy. An example of an existence pattern).
The third is a normal pattern P3 (corresponding to the third existence pattern) that is in a state other than the circulation pattern and the separation pattern.
For example, the image processing apparatus 6 determines whether or not the start end and the end in the Y-axis direction of the range represented by the fillet coordinates coincide with the start and end of the all-around range Wy for a certain label value, respectively. To do. Further, if they match, the image processing device 6 determines whether or not the target label value exists in both of the ranges obtained by dividing the entire circumference range Wy into two equal parts. As a result of the determination, when the target label value exists in both ranges, the image processing device 6 determines that the target label value is the circulation pattern, and otherwise, the separation pattern. It is determined that
In addition, when the start end and end in the Y-axis direction of the range indicated by the fillet coordinates of the target label value do not coincide with the start end and end of the entire circumference range Wy, the image processing device 6 The label value to be determined is the normal pattern.

[Step S8]
Next, the image processing device 6 sets (selects) the X-axis coordinates one by one, and from the label distribution information stored in the frame memory, one line in the Y-axis direction in the set X-axis coordinates. Is sampled (selected) as information used for the mask range setting process (S8). Thereafter, the image processing apparatus 6 executes the processes of steps S9 to S14 described later every time the label value information for one line in the Y-axis direction is sampled.
The X-axis coordinates set for the sampling may be all coordinates (pixels) in a range occupied by the surface height distribution information in the X-axis direction or predetermined according to the spatial resolution required for the shape defect inspection. It can be considered that some coordinates (pixels) are thinned out at intervals. Within the range of the spatial resolution allowed for shape defect inspection, it is preferable that the setting interval of the X-axis coordinates is large because the calculation load can be suppressed.

Next, the image processing device 6 determines, for each line in the Y-axis direction sampled in step S8, the determination result of the presence pattern for each label value existing on that line and the position of the label value. Accordingly, the mask range setting step for setting the mask range for one line in the Y-axis direction and storing the coordinates in the built-in memory is executed (S9-12, S13 or S14).
Specific examples will be described below.

[Steps S9 to S12]
First, when the presence pattern (discrimination result) of the target label value is the circulation pattern P1 (first presence pattern), the image processing device 6 counts the number of label values for the target label value. Then, it is determined whether or not the number is equal to or greater than a preset number (set number) (S10).
When it is determined that the number of the label values of interest is equal to or greater than the set number, the image processing device 6 performs all the sampling for one line in the Y-axis direction at that time (the entire circumference range). Wy) is set to the mask range (S11).
On the other hand, when it is determined that the number of the target label values is less than the set number, the image processing device 6 determines the target label value for one line in the Y-axis direction sampled at that time. Only the position where is present is set as the mask range (S12).

[Step S13]
On the other hand, when the existence pattern (discrimination result) of the target label value is the separation pattern P2 (second existence pattern), the image processing device 6 uses the entire circumference range Wy for the target label value. In each of the bisected ranges, a range from each end position of the entire circumference range Wy to the position of the target label value farthest from the position (start end position or end position) is set as the mask range (S13). ).
That is, in the range from the start end position to the intermediate position of the entire circumference range Wy, a range starting from the start end position and ending at the position of the target label value closest to the intermediate position is set as the mask range. The Further, in the range from the intermediate position to the end position of the entire circumference range Wy, a range starting from the position of the target label value closest to the intermediate position and having the end position as the end point is also set as the mask range. The

[Step S14]
Further, the image processing device 6 sets, as the mask range, a range over the entire position where the target label value exists when the target pattern value presence pattern (determination result) is the normal pattern P3. (S14).
That is, a range in which the position of the target label value closest to the start position of the entire circumference range Wy is a start point and the position of the target label value closest to the end position of the entire periphery range Wy is an end point is Set as mask range.
The processing of steps S9 to S14 shown above is executed for each sampled label value on one line in the Y-axis direction, and a range obtained by ORing the mask ranges set for each label value is It is set as the final mask range in one line.

[Step S15]
Thereafter, the image processing device 6 performs control so that the processing in steps S8 to S14 described above is repeated until sampling (S8) for all coordinates of the X axis is completed (S15). Thereby, coordinate information of all the mask ranges related to the sidewall surface of the sample of the tire 1 is obtained.
Then, the image processing device 6 transfers the coordinate information of all the mask ranges related to the sidewall surface of the sample of the tire 1 to the host computer 7 that executes the shape defect inspection processing using the coordinate information.
As described above, the image processing device 6 automatically detects the position of the display mark M, which is an uneven mark, based on the surface height distribution information obtained from the sample of the tire 1 (S2 to S2). S6), the coordinate information of the mask range surrounding the range where the display mark M exists is automatically set (S7 to S15). Steps S2 to S15 executed by the image processing device 6 are an example of the mask range automatic setting step. The image processing device 6 is an example of a processor.

[Step S16]
By the way, the above-described mask range automatic setting process (S2 to S15) as described above automatically ensures that the mask range surrounding the display mark M is automatically and without exception for all types of tires 1 having various surface shapes. It is very difficult to set up.
Therefore, the host computer 7 executes a process of correcting the coordinate information of the mask range automatically set in the mask range automatic setting step (S2 to S15) according to the operation of the operator.
That is, the host computer 7 that has obtained the normalized surface height distribution information and the mask range coordinate information executes the following mask range manual correction processing and collation data designation processing (S16). ). In the following description, the surface height distribution information obtained from the sample of the tire 1 is referred to as sample surface shape information. This sample surface shape information is the normalized surface height distribution information transmitted from the image processing device 6 to the host computer 7 in step S1.
The mask range manual correction process is a process for executing the following image output process and mask range change process in parallel.
The image output process is a process in which the surface shape image of the sidewall surface of the tire 1 based on the sample surface shape information and the mask range image based on the coordinate information of the mask range are superimposed and displayed on the first display device 73. .
The mask range changing process is a process of changing (correcting) the coordinate information of the mask range in accordance with an operation input through the operation unit 72.

10 to 13 are first to fourth examples of display screens of the display device 73 by the image output process. FIG. 11 is an enlarged view of a part of the display screen.
As shown in FIG. 10, the host computer 7 superimposes a surface shape image g1 based on the sample surface shape information and a mask range image g2 based on the coordinate information of the mask range on the display device 73. .
The surface shape image g1 is, for example, an image in which the brightness of the corresponding pixel is different or the display color is different depending on the size of the surface height measurement value in the sample surface shape information.
The mask range image g2 is, for example, an image of a frame line that forms the outline of the mask range, an image in which the mask range is filled with a predetermined color, or the like. The mask range image g2 shown in FIG. 10 is an image in which a frame line forming the outline of the mask range is displayed with a broken line.
By the image output process, the operator of the host computer 7 can visually confirm the automatically set mask range on the surface shape image g1 of the sidewall surface of the tire.
Further, as shown in FIG. 10, the host computer 7 superimposes a cursor image g3 whose display position moves in response to an operation input on the operation unit 7 on the surface shape image g1 in the image output process. It is displayed on the display device 73. For example, the host computer 7 moves the display position of the cursor image g3 according to the operation of the mouse in the operation unit 7.

Further, the host computer 7 changes the coordinate information of the mask range for the coordinates corresponding to the display position of the cursor image g3 in the mask range changing process. A specific example of the mask range changing process will be described below.
For example, as shown in FIG. 10, the host computer 7 selects the mask range corresponding to the display position of the cursor image g3, and designates information on the selected mask range through an operation on the operation unit 72. Execute the process to add the changed. For example, the host computer 7 executes processing for canceling the selected mask range, processing for expanding the selected mask range, processing for compressing the selected mask range, and the like. Here, canceling the mask range means that the corresponding range is not excluded from the shape defect inspection target.
Thus, for example, when the erroneous mask range is set in a portion other than the display mark M due to, for example, a sample of the tire 1 having a shape defect on its sidewall surface, the mask The range setting can be canceled.

Further, as shown in FIG. 11, the host computer 7 sets the mask range only for the range that overlaps the range specified by the movement operation of the cursor image g3 among the already set mask ranges. The process of canceling is also executed.
As a result, for example, the display mark M and the shape defect are close to each other on the sidewall surface of the sample of the tire 1, so that the mask range is automatically set in a range wider than the range to be originally set. When set, the setting can be canceled for an extra part of the mask range.
Further, as shown in FIG. 12, the host computer 7 also executes a process of setting a range other than the range specified by the movement operation of the cursor image g3 as the mask range.
Thus, for example, when the surface height distribution information includes information on a portion other than the sidewall surface, such as a tire tread surface and a rim portion, the extra portion is excluded from the range to be inspected. Can be excluded.
As described above, the manual operation in the mask range changing process only needs to be performed on a part of the mask range that is automatically set. Therefore, the manual operation in the mask range changing process is much simpler than the operation of manually setting the mask range one by one for all the many uneven marks present on the sidewall surface. Work.

The host computer 7 also executes the collation data designation process in conjunction with the mask range manual correction process. According to the operation input through the operation unit 72, the collation data designation processing includes a part of the surface height measurement value of the surface height distribution information about the sample of the tire 1 and coordinates of the measurement value. This is a process of identifying collation data as information and recording the collation data in the data storage means in the computer main body 7. The surface height measurement value included in the verification data is an example of the registered surface shape information.
For example, as shown in FIG. 13, the host computer 7 determines the specific coordinates of one or more specific coordinates in the X-axis direction specified by the movement operation of the cursor image g3 and the determination operation of the movement destination. The surface height measurement value and its coordinates for one line in the Y-axis direction in each are recorded in the data storage means as the verification data. Hereinafter, the specific coordinates are referred to as collation position coordinates. The surface height measurement value for one line in the Y-axis direction means the surface height measurement value over the entire Y-axis direction.
A one-dot chain line shown in FIG. 13 is a line displayed on the display device 73 so as to overlap the surface shape image g1, and represents an array position of the surface height measurement values included in the verification data. As the verification data, data of a characteristic uneven portion on the sidewall surface of the tire 1 is designated.
The verification data is used for detecting a deviation between the coordinate system of the coordinate information of the mask range and the coordinate system of the surface height distribution information obtained for each tire 1 to be inspected.
The example shown in FIG. 13 is an example in which one set or a plurality of sets of data for one line in the Y-axis direction are set as the verification data. In addition, the collation data may be all of the surface height distribution information about the sample of the tire 1 or data in a part of a two-dimensional area designated by an operation input.

[Step S17]
Then, the host computer 7 indicates the type of the tire 1 by using the corrected mask range coordinate information and the verification data obtained by the mask range manual correction process and the verification data designation process (S16). In association with the identification code, it is recorded in the data storage means of the computer main body 71 (S17). Thereby, the mask range setting process ends.

Next, an example of the procedure of the shape defect inspection process for each tire 1 to be inspected executed by the image processing device 6 and the host computer 7 will be described with reference to the flowchart shown in FIG. Before the processing shown in FIG. 9 is executed, the shape measurement processing is executed as described above, and the surface height distribution information about the sidewall surface of each tire 1 to be inspected is stored in the frame of the image processing device 6. Assume that it is stored in memory.
[Step S21]
First, the image processing device 6 executes a measurement value normalization step (S1) for the surface height distribution information on the sidewall surface, as in step S1 described above.

[Steps S22 and S23]
Next, the image processing device 6 calculates the surface height distribution information obtained for each tire 1 to be inspected and the coordinate information of the mask range obtained from the sample of the tire 1 obtained from the host computer 7. The process which detects the shift | offset | difference of a coordinate system between is performed (S22, coordinate system shift | offset | difference detection process). Hereinafter, the surface height distribution information obtained for each tire 1 to be inspected is referred to as inspection surface shape information.
Here, it goes without saying that the tire 1 to be inspected and the sample of the tire 1 that is the object from which the coordinate information of the mask range used for the inspection is obtained are the same type of tire. Prior to the processing in step S22, the host computer 7 determines the coordinate information of the mask range obtained from the tire 1 of the same type as the tire 1 to be inspected based on the identification code indicating the type of the tire 1 and the collation. Data is retrieved and the retrieval result is delivered to the image processing device 6.
In step S22, the image processing apparatus 6 determines the surface height measurement value for one line in the Y-axis direction and the Y-axis in the verification data at the verification position coordinates in the X-axis direction in the inspection surface shape information. The surface height measurement value for one line in the direction is collated while shifting the position in the Y-axis direction, and the shift width when the difference between them is minimized is detected as the deviation of the coordinate system in the Y-axis direction. To do. When the verification data includes the surface height measurement values for one line in a plurality of sets in the Y-axis direction, the shift width when the sum of the differences in each set is minimized is the Y-axis direction. Is detected as a shift of the coordinate system.
Then, the image processing device 6 corrects the coordinate information of the mask range so that the deviation of the coordinate system detected in step S22 is eliminated (S23).

[Step S24]
Next, the image processing device 6 executes a mask range interpolation step (S24) shown below.
In the mask range interpolation step, the image processing device 6 first calculates the surface within the mask range from the surface height measurement value outside the mask range in the inspection surface shape information for each line in the Y-axis direction. Calculate the interpolated value of the height measurement value. This interpolated value is a slowly changing value, and a linear interpolated value is a typical example, but it may be a quadratic curve interpolated value or the like.
Further, the image processing device 6 measures the surface height measurement value within the mask range in the surface shape information for inspection stored in the frame memory for each line in the Y-axis direction. The value is replaced with an interpolated value and stored in the frame memory. As described above, the inspection surface shape information after the interpolation processing, that is, the inspection surface shape information after the processing for replacing the surface height measurement value in the mask range with the interpolation value is performed. Used for shape defect inspection processing by the computer 7.

[Step S25]
Then, the host computer 7 executes a shape defect inspection process for the sidewall surface of the tire 1 in accordance with a predetermined rule using the inspection surface shape information after the interpolation process (S25). Hereinafter, an example will be described. The following example does not constitute a feature of the present invention.
First, the host computer 7 obtains information on measured values (partly including the interpolation value) for one line in the Y-axis direction from the surface shape information for inspection after interpolation processing for shape defect inspection. Sampling (selection) as a target.
Then, the host computer 7 calculates, for example, the following first index value as an index value of the local unevenness defect (the bulge or the dent).
First, low-pass filter processing by FFT of a predetermined order (for example, 50th order) or less is performed on the measurement value for one line in the Y-axis direction.
Then, with respect to the measurement value after the low-pass filter processing, the maximum of the measurement value within the range of the window while scanning the window with an angle range of about 7 ° as compared to the angle range of 360 ° of the entire measurement value The difference between the value and the minimum value is calculated, and this is used as the first index value. When the first index value is greater than a predetermined value, it is determined that the tire has a shape defect.
Further, the host computer 7 calculates, for example, a second index value shown below as an index value for a defect inspection (referred to as a runout inspection) of a gentle unevenness change of the entire tire circumference.
First, low-pass filter processing by FFT of a predetermined order (for example, 15th order) or less is performed on the measurement value for one line in the Y-axis direction.
Then, a difference between the maximum value and the minimum value in the entire measurement value after the low-pass filter processing is calculated, and this is used as the second index value. When the second index value is greater than a predetermined value, it is determined that the tire has a shape defect.
When only a part of the coordinates (lines) in the range occupied by the inspection surface shape information in the X-axis direction is specified in advance as the inspection target area, the specified line in the inspection surface shape information is specified. Only the process of step S25 is executed. For example, the inspection target area may be specified in the same manner as the specification of the verification data (see FIG. 13).

FIG. 6 is a diagram illustrating an example of the mask range set based on the corrected binary distribution information imaged in FIG. 5 by the processing of steps S8 to S16 as a binary image. In FIG. 6, the white part is the mask range.
In the tire shape inspection apparatus W, a well-known Sobel filter process is applied to the normalized surface height distribution information, which is two-dimensional information, with the two-dimensional information (S2). Thereby, even if the edge part (contour part) of the display mark M is formed extending in any direction in the two-dimensional coordinates, the edge part is reliably detected.
In addition, by the labeling process (S5), the same label value is set for each edge portion of the series of display marks M isolated from others, and the shape defect is determined based on the fillet coordinates (for each same label value) of the edge portion. The mask range to be excluded from the inspection target is set (S6 to S14). Thereby, the measurement value of the non-protruding portion located within the outline of the display mark M is also excluded from the shape defect inspection target, and it can be avoided that the non-protruding portion is erroneously detected as a shape defect portion. In FIG. 6, the inside of the outline of the display mark M representing the characters “A”, “B”, “W”, etc. is set as the mask range.
Further, in the tire shape inspection apparatus W, the measured value within the mask range in the inspection surface shape information is replaced with the interpolated value that changes gradually (S24). Therefore, even if the shape defect inspection process is executed with the same algorithm regardless of the presence or absence of the mask range, it can be avoided that the shape within the mask range is a shape defect portion.
That is, the process of step S24 is a process of replacing the surface height measurement value in a range corresponding to the coordinate information of the mask range in the inspection surface shape information with an interpolation value based on the surface height measurement value other than the range. It is. Then, in the step S24, the surface height in a range corresponding to the coordinate information of the mask range in the inspection surface shape information is corrected after correcting the shift of the coordinate system by the replacement process with the interpolation value. It is an example of the mask range test | inspection exclusion process which excludes a thickness measurement value from the object of a shape defect inspection process.

  In addition, since labeling (S5) is performed on the binary distribution information subjected to the expansion process (S4), the rise (change) of the surface height is relatively moderate at a part of the outline of the display mark M. Even if such a part is included, the part is recognized as a part included in the outline of the display mark M. Thereby, it can be avoided that a part of the outline of the display mark M is erroneously detected as a shape defect portion.

In the embodiment described above, the mask range is set for each line in the Y-axis direction based on the fillet coordinates of the label value by the processing of steps S8 to S14 shown in FIG.
On the other hand, as another embodiment of the present invention, instead of the processing in steps S8 to S14 shown in FIG. 3, each rectangular range specified by the fillet coordinate for each label value may be set as the mask range. It is done. In this case, in the labeling step (S5), the labeling process may be performed without the assumption that the coordinates (Y coordinates) at both ends of the entire circumference range Wy are adjacent coordinates in the Y-axis direction.
FIG. 7 shows a binary image of the mask range when the rectangular range specified by the fillet coordinates for each label value is set as the mask range based on the corrected binary distribution information imaged in FIG. FIG.
However, in order to set the mask range more finely, it is preferable to adopt the processing of steps S8 to S14 shown in FIG.

The tire shape inspection device W may perform the processing of steps S2 to S15 with a high calculation load for one sample for each type of tire 1.
Further, the processing of steps S21 to S25 executed for each tire 1 to be inspected is processing with a relatively low calculation load by the processor, and can be executed at high speed even by a practical processor.
Further, the tire shape inspection apparatus W performs the steps S16 and S17, and visually confirms the mask range automatically set on the surface shape image g1 of the sidewall surface, and determines the mask range. It can be corrected by a very simple manual operation. Therefore, the correct mask range can be reliably set for many types of tires having various surface shapes.
Therefore, when the tire shape inspection apparatus W inspects the shape defect of the sidewall surface of the tire 1 on which the uneven display mark M is formed, the display mark M is formed from the surface height measurement value. A process for reliably and mistakenly measuring a measured value within a certain range can be executed at high speed. As a result, according to the tire shape inspection apparatus W, a correct shape defect inspection can be performed in a short time.

The embodiment described above is an embodiment in which the processing of steps S1 to S17 and steps S21 to S25 are shared and executed by the image processing device 6 and the host computer 7 which are examples of processors, respectively. .
However, for example, an embodiment in which the host computer 7 executes all of the steps S1 to S17 and the steps S21 to S25 is also conceivable. Further, an embodiment in which the processes of steps S1 to S17 and steps S21 to S25 are shared and executed by three or more processors is also conceivable.

 The present invention can be used for a tire shape inspection apparatus.

W: Tire shape inspection device M: Display mark (mark with unevenness)
Wy: Full-circle range 1: Tire 2: Tire rotating machine 3: Sensor unit 4: Unit driving device 5: Encoder 6: Image processing device 7: Host computer 10: Projection device 11, 12, 13: Line light source 20: Camera 21: Image sensor 22: Camera lens

Claims (6)

The surface height measurement value at each position over the entire circumferential range of the sidewall surface measured by scanning the sidewall surface of the tire on which the uneven marks are formed in the circumferential direction represents the radial direction of the tire. A tire shape for performing shape defect inspection processing on the sidewall surface based on surface height distribution information arranged in a two-dimensional coordinate system comprising one coordinate axis and a second coordinate axis representing the circumferential direction of the tire An inspection method,
The position of the uneven mark is automatically detected based on the sample surface shape information which is the surface height distribution information obtained from the tire sample determined for each type of the tire to be inspected, and the uneven A mask range automatic setting process for automatically setting the coordinate information of the mask range surrounding the range where the mark exists,
An image output step of displaying on the display means a surface shape image based on the sample surface shape information and a mask range image based on the coordinate information of the mask range;
In parallel with the image output step, a mask range changing step for changing the coordinate information of the mask range in accordance with an operation input through a predetermined operation unit;
An information registration step of storing in the storage means the coordinate information of the mask range after the change by the mask range change step and the registered surface shape information which is a part or all of the sample surface shape information;
By comparing the surface shape information for inspection, which is the surface height distribution information obtained for each tire to be inspected, with the registered surface shape information, between the surface shape information for inspection and the coordinate information of the mask range A coordinate system deviation detection step for detecting a coordinate system deviation;
Mask range inspection for correcting the deviation of the coordinate system and excluding the surface height measurement value in the range corresponding to the coordinate information of the mask range in the surface shape information for inspection from the object of shape defect inspection processing An exclusion process;
A tire shape inspection method comprising: The registered surface shape information is the surface height measurement value over the entire direction of the second coordinate axis at a specific coordinate in the direction of the first coordinate axis in the sample surface shape information;
The coordinate system deviation detecting step includes the surface height measurement value and the registered surface shape information over the entire direction of the second coordinate axis at the specific coordinate in the direction of the first coordinate axis in the surface shape information for inspection. The tire shape inspection method according to claim 1, further comprising a step of detecting a shift of the coordinate system in the second coordinate axis direction by collating the position in the second coordinate axis direction while shifting the position. . The mask range automatic setting step includes:
A two-dimensional edge detection step of detecting an edge of the uneven mark by two-dimensional edge detection processing for the sample surface shape information, and storing the detected two-dimensional edge distribution information in a storage means;
A labeling step of applying a labeling process to the two-dimensional edge distribution information and storing the label distribution information as a result of the processing in a storage unit;
A mask range setting step of setting the coordinates of the mask range surrounding the existence range of the uneven marks based on the fillet coordinates for each label value in the label distribution information, and storing the coordinates in a storage means. The tire shape inspection method according to claim 1 or 2. The image output step includes a step of causing the display means to display a cursor image whose display position moves in response to the operation input;
The tire shape inspection method according to any one of claims 1 to 3, wherein the mask range changing step includes a step of changing coordinate information of the mask range with respect to coordinates corresponding to a display position of the cursor image. The mask range inspection excluding step includes
5. The step of replacing the surface height measurement value in a range corresponding to the coordinate information of the mask range in the inspection surface shape information with an interpolated value based on the surface height measurement value other than the range. The tire shape inspection method according to any one of the above. Irradiation of line light onto the sidewall surface of the relatively rotating tire on which the uneven marks are formed and imaging of the image of the line light are used for the inspection of the shape defect of the tire based on the captured image. A tire shape inspection device for deriving surface height distribution information,
Line light irradiating means for continuously irradiating a plurality of line lights from a direction different from the detection height direction in the light cutting line so that one light cutting line is formed on the sidewall surface;
Imaging means for capturing images of the plurality of line lights irradiated on the sidewall surface in a direction in which principal rays of the plurality of line lights are regularly reflected with respect to the sidewall surface;
A light cutting method shape detection means for deriving surface height distribution information over the entire circumference of the sidewall surface by detecting a light cutting line in a captured image of the imaging means;
The position of the uneven mark is automatically detected based on the sample surface shape information which is the surface height distribution information obtained from the tire sample determined for each type of the tire to be inspected, and the uneven Mask range automatic setting means for automatically setting the coordinate information of the mask range surrounding the existing range of the mark;
An image output means for displaying on the display means a surface shape image based on the sample surface shape information and a mask range image based on the coordinate information of the mask range;
In parallel with the processing of the image output means, a mask range changing means for changing the coordinate information of the mask range in response to an operation input through a predetermined operation section;
Information registration means for storing in the storage means the coordinate information of the mask range after the change by the mask range changing means and the registered surface shape information which is a part or all of the sample surface shape information;
By comparing the surface shape information for inspection, which is the surface height distribution information obtained for each tire to be inspected, with the registered surface shape information, between the surface shape information for inspection and the coordinate information of the mask range A coordinate system deviation detecting means for detecting a deviation of the coordinate system;
Mask range inspection for correcting the deviation of the coordinate system and excluding the surface height measurement value in the range corresponding to the coordinate information of the mask range in the surface shape information for inspection from the object of shape defect inspection processing Exclusion means,
A tire shape inspection apparatus comprising:

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