JP2010071875A - Defect inspecting method and defect inspecting device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a technology that does not cause incorrect recognition even when an image is mechanically divided into a plurality of regions and inspected, when an end region of an inspecting object is included in the image. <P>SOLUTION: The image of the inspecting object is divided into a plurality of blocks, and an optional attention block B(m, n) is set in them. The concentration of the attention block B(m, n) is compared with that of an adjacent peripheral block of each of directions A1-A4. At this time, the total value of variation values is handled as a defective degree only when the concentration variation amount from one peripheral block B(m-1, n) to the attention block B(m, n) becomes a negative value in the view of the lateral directions, for example, and the concentration variation amount from the attention block B(m, n) to the other peripheral block B(m+1, n) becomes a positive value. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a defect inspection method and a defect inspection apparatus capable of inspecting a surface defect using an image obtained by imaging an inspection object.

  Conventionally, regarding this type of defect inspection method or defect inspection apparatus, image data obtained by imaging an inspection object is divided into arbitrary regions, and the integrated value is obtained by integrating the density values of pixels included in the region. A prior art for detecting a defect by comparing this integrated value with a threshold value is known (see Patent Document 1). As another prior art, the pixel density is integrated in an arbitrarily divided region, a differential operation is performed for each region interval from the integrated value, a differential value is obtained, and the value is compared with a predetermined value to detect a defect. An inspection device for detection is known (see Patent Document 2).

According to the above-described prior art, instead of checking one pixel at a time for every pixel in the image data obtained by imaging, the value obtained by averaging the density of the pixels included in the region is obtained by integration. Therefore, it is considered that the calculation process can be speeded up accordingly.
JP 2005-265467 A JP 2000-36048 A

  However, for example, when the edge region of the object to be inspected is included in the captured image, if the entire image is divided into arbitrary regions as in the above-described prior art, the background image outside the edge portion May have a significant effect on the average density of the entire region. In this case, even if there is no defect in the end region of the inspection object, there is a problem that the entire region is erroneously recognized as a defect (so-called overdetection).

  Therefore, the present invention does not cause misrecognition even when the image includes the edge region of the object to be inspected, even if the image is mechanically divided into a plurality of regions for inspection. The issue is the provision of technology.

1stly this invention is a defect inspection method comprised from the process of the following (1)-(4).
(1) Acquisition process In this process, the image which imaged the to-be-inspected object is acquired. Note that the acquired image may be reproduced using image data stored in advance.

(2) Division Step In this step, the acquired image is divided into a plurality of blocks in a predetermined arrangement. As a result, each divided block includes a plurality of pixels constituting the image.

(3) Comparison process In this process, a target block to be inspected is set among a plurality of divided blocks, and the density of the image viewed in the target block and the image of the image viewed in the neighboring blocks adjacent to the target block are set. The density is compared for each of two neighboring blocks adjacent in one direction in the array.

(4) Calculation step In this step, as a result of comparing the densities in the comparison step (3) above, only when the density in the target block is lower than the density in the two neighboring blocks, the target block The defect degree of the block of interest is obtained by adding the two variations obtained between the density in the block and the density in each peripheral block.

(5) Determination process In this process, the presence or absence of a defect in the block of interest is determined based on the obtained defect degree. For example, a predetermined threshold value is compared with the defect degree, and if the defect degree exceeds the threshold value, it is determined that there is a defect, and conversely, if the defect degree does not reach the threshold value, it can be determined that there is no defect.

  As described above, according to the defect inspection method of the present invention, an arbitrary target block is set from among the original image divided into a plurality of blocks, and the density is compared between each of the neighboring blocks and the adjacent two neighboring blocks. In some cases, the inspection is performed only when the density of the target block is lower than the density of the two neighboring blocks. For this reason, when the density of the target block is higher than the density of one of the peripheral blocks or higher than both of the peripheral blocks, the presence / absence of a defect in the target block is not determined.

  Therefore, according to the defect inspection method of the present invention, when a block including many pixels imaged outside the range of the inspection object is set as a target block, one of the two neighboring blocks adjacent to the block is Depending on the direction, the image always includes many pixels imaged outside the range of the object to be inspected, like the target block. Therefore, in this case, the density of the block of interest will not be lower than the density of both peripheral blocks, so even if the density of the block of interest is extremely low, it will be mistakenly recognized as being defective. There is no end to it.

  Secondly, the defect inspection method of the present invention sets the plurality of blocks divided in the division step (2) as the target block in order, thereby comparing each block with the comparison step (3) and the calculation (4). The process and the determination process of (5) are executed.

  In this case, for example, since each process is executed for all the blocks, the inspection can be performed over the entire image.

  Thirdly, in the defect inspection method of the present invention, in the comparison step (3), the difference in density in the target block with respect to the density in one peripheral block of the two peripheral blocks is determined as the first change amount. The difference in density in the other peripheral block with respect to the density in the target block can be obtained as the second change amount.

  In this case, in the calculation step (4) above, the first change amount and the second change amount are obtained only when the first change amount takes a negative value and the second change amount takes a positive value. The difference from the change amount can be used as the defect degree.

  By adopting the above method, it is possible to calculate the degree of defect from the first and second amount of change obtained mechanically, or to determine whether or not to calculate the degree of defect, so it is simple and easy The arithmetic processing can be advanced.

  Fourthly, in the defect inspection method of the present invention, in the comparison step (3) above, for one target block, a set of two neighboring blocks adjacent to the target block is defined in a plurality of directions within the array. The concentration may be compared in multiple ways.

  In this case, in the calculation step (4) above, the two change amounts obtained between the density in the block of interest and the density in each peripheral block are added together for each direction in which the comparison is performed, The maximum amount of change can be set as the defect degree of the block of interest.

  If such a method is adopted, the result obtained in the direction in which the density change is most noticeable can be used as the defect degree (representative value) of the block of interest. More accurate inspection can be performed after eliminating the influence of noise other than defects.

  Fifth, in the defect inspection method of the present invention, in the division step (2), it is preferable to change the shape of the block into which the image is divided according to the type of defect to be detected.

  In this case, even with respect to a plurality of types of defects having features in each direction in the image, it is possible to perform an appropriate inspection while narrowing down as much as possible.

  Sixth, the present invention provides a defect inspection apparatus. This defect inspection apparatus can execute the first to fifth defect inspection methods.

  That is, the defect inspection apparatus divides an image generated by the image generation unit by capturing an object to be inspected, and an image generated by the image generation unit into a plurality of blocks in a predetermined arrangement, and set the inspection target therein Comparison means for comparing the image density seen in the target block and the image density seen in the peripheral block adjacent to the target block for each of two peripheral blocks adjacent in one direction in the array, and comparison by the comparison means As a result, only when the density in the target block is lower than the density in the two neighboring blocks, the two changes found between the density in the target block and the density in each peripheral block Calculating means for calculating the defect degree of the block of interest by adding the amounts; and determining means for determining the presence or absence of a defect in the block of interest based on the defect degree obtained by the calculation means It is obtain things.

  For this reason, the defect inspection apparatus according to the present invention, for example, sequentially captures a large number of objects to be inspected to generate images, and sequentially uses this generated image to perform block division, density comparison, defect degree calculation, and presence / absence of defects. It is possible to execute a procedure such as determination in a flow work. Therefore, for example, it is possible to obtain usefulness such as efficiently inspecting an object to be mass-produced on a production line and contributing to quality control thereof.

  As described above, according to the defect inspection method and the defect inspection apparatus of the present invention, even if a pixel captured outside the range of the inspection object is included in the image, this is not erroneously recognized as a defective portion. . Therefore, it is not necessary to exclude pixels outside the range of the object to be inspected from the image in advance, and the inspection can be advanced mechanically using the image, so that the inspection efficiency can be increased accordingly.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a diagram schematically illustrating a configuration example of the defect inspection apparatus 10. The defect inspection method can be realized by using this defect inspection apparatus 10. Here, first, a configuration row of the defect inspection apparatus 10 will be described.

  The defect inspection apparatus 10 includes a belt conveyor 12 as a transport mechanism, for example. This belt conveyor 12 is an object to be inspected (hereinafter referred to as “work”) W placed on its conveying surface while causing the endless conveying belt 12a to circulate in a certain direction as the roller 12b rotates. Is conveyed in the traveling direction of the conveyor belt 12a. The belt conveyor 12 includes a motor (not shown) as its power.

  In the belt conveyor 12, for example, a rotary encoder 14 is connected to one roller 12b. The rotary encoder 14 is driven by the rotation of the roller 12b via the transmission belt 16, for example, and generates a pulse signal at every fixed rotation angle.

  The defect inspection apparatus 10 includes a line sensor 18 as an imaging device, and the line sensor 18 is installed above the belt conveyor 12. The line sensor 18 incorporates a large number of imaging elements arranged in a straight line with a certain width. For the image sensing device used for the line sensor 18, an electron tube type imaging tube or the like can be used in addition to a semiconductor type such as a CCD image sensor or a CMOS image sensor. In addition, a lens 20 is installed on the front surface of the line sensor 18 (in front of the light receiving surface) so that the reflected light from the work W is imaged on the image sensor.

  In addition, the defect inspection apparatus 10 includes a lighting fixture 22 as a light source that illuminates the workpiece W. The lighting fixture 22 is also installed above the belt conveyor 12, and the lighting fixture 22, the line sensor 18, and the lens 20 are positioned opposite to each other across the workpiece W at the imaging position in FIG. . Various kinds of illumination lamps such as a cold cathode tube, a discharge tube, and an incandescent lamp can be used for the illumination fixture 22.

  In addition, a slit (light-shielding plate) 50 is disposed in the light irradiation direction of the luminaire 22, and the slit 50 has an edge parallel to the main scanning direction. The slit 50 is provided at a position where the light emitted from the luminaire 22 is partially blocked (about 50%) up to the edge at one end thereof. Such a slit 50 has a function of enhancing the contrast of an image when a defective portion (uneven defect) of the workpiece W is imaged. A defect inspection method using the slit 50 will be described later.

  Each roller 12b of the belt conveyor 12 receives power from a drive source such as a motor (not shown) and rotates in the same direction (clockwise as viewed in FIG. 1) at a constant speed. Further, the work W is supplied to the belt conveyor 12 one by one from an auto feeder (not shown), for example. Here, for the workpiece W to be inspected (inspected), various types such as resin, metal, wooden plate-like piece, paper piece, printed matter, box-like body, etc. can be applied. It is not restricted to only. In addition, the workpiece | work W discharged | emitted from the terminal end of the belt conveyor 12 is collect | recovered by the stacker which is not illustrated, for example, or it is taken over by another conveyor.

  The defect inspection apparatus 10 includes an arithmetic processing unit 24 in addition to these mechanical elements. The arithmetic processing unit 24 is configured using an electronic computer having a central processing unit (CPU), a storage device (ROM, RAM), an input / output interface (I / O), and the like. Examples of electronic computers include general-purpose personal computers and workstations. The arithmetic processing unit 24 receives a pulse signal from the rotary encoder 14 and an image signal from the line sensor 18. The arithmetic processing unit 24 performs various processes on the image data based on these input signals. The processing performed by the arithmetic processing unit 24 will be further described later.

  FIG. 2 is a perspective view illustrating a process in which the workpiece W is imaged by the line sensor 18. The line sensor 18 has its main scanning line L positioned in the width direction (transverse direction) of the transport belt 12a, and the illuminating device 22 emits illumination light centering on the main scanning line L. The work W passes through the main scanning line L in the process of being conveyed, and the light reflected from the surface of the work W at the position of the main scanning line L passes through the lens 20 and forms an image on the image sensor of the line sensor 18. . At this time, the main scanning line L includes not only the surface area of the workpiece W but also the upper surface area of the conveying belt 12a on both outer sides thereof.

  FIG. 3 is a block diagram showing the configuration of the arithmetic processing unit 24 in more detail. The arithmetic processing unit 24 includes an input unit 30, a data processing unit 32, a data storage unit 34, a defect detection unit 36, and an output unit 38 as its constituent elements, and a CPU 40 that controls these operations. Hereinafter, each component will be described. In this example, the data processing unit 32 and the defect detection unit 36 are provided separately from the CPU 40. However, the functions of the data processing unit 32 and the defect detection unit 36 may be realized using the resources of the CPU 40. Good. In addition, the configuration described here with reference to the drawing is merely a preferable example, and another configuration may be adopted for the arithmetic processing unit 24.

  An image signal from the line sensor 18 and a pulse signal from the rotary encoder 14 are input to the input unit 30 via the input interface. The input unit 30 has a function of performing attenuation adjustment on an image signal (analog signal) as necessary while referring to an input pulse signal, A / D converting, and transmitting it to the data processing unit 32.

  The data processing unit 32 processes the transmitted image signal in the form of image data (raster data) for one line. At this time, for example, gray scale density values (0 to 255 gradations) are assigned to each pixel in one line. The data processing unit 32 assigns, for example, a column address from 0 to m to each pixel included in one line, and assigns a row address from 1 to n, for example, for each line.

  The image data for each line processed by the data processing unit 32 is transferred to the data storage unit 34 via the data bus 42. A fixed storage area is secured in the data storage unit 34, and memory addresses respectively corresponding to the row address and the column address assigned to the image data are allocated to this storage area. The transferred image data (density value of 0 to 255) is written in the memory cell (for 1 byte) at the corresponding memory address.

  The defect detection unit 36 accesses the data storage unit 34 via the data bus 42, reads image data from the storage area, and performs defect inspection processing. The defect detection unit 36 writes defect inspection result data in the data storage unit 34 and transfers the inspection result data to the output unit 38 as necessary. A specific aspect of the inspection process performed by the defect detection unit 36 will be further described later.

  In addition to outputting the inspection result data as described above, the output unit 38 outputs imaged image data (image of the workpiece W) as necessary. The data output destination is, for example, an image display device (not shown), another general-purpose computer, a recording medium, or the like. Accordingly, the inspection result can be displayed on the image display device so that the operator can visually recognize the defect on the screen, or the surface image of the workpiece W can be displayed on the screen and the operator can visually recognize the image. .

  In addition, although not particularly illustrated, the arithmetic processing unit 24 may include a data correction unit. The data correction unit reads the image data stored in the data storage unit 34 and performs the correction process. The correction process is performed, for example, for the purpose of correcting unevenness in density due to a periodic change in the conveyance speed by the belt conveyor 12, or correcting changes in density difference due to flickering in room lighting. Such a function of the data correction unit may be realized using resources of the CPU 40.

[Generation of image data]
In the arithmetic processing unit 24, image data is generated through the following process, for example. In other words, when a pulse signal is input (edge turn-on) from the rotary encoder 14, the arithmetic processing unit 24 performs imaging (imaging) for one line using the line sensor 18. Thereby, in the line sensor 18, each image sensor is activated (exposure state), and an image signal is generated based on the intensity of reflected light from the main scanning line L. Next, when a pulse signal is input (edge turn-off), the arithmetic processing unit 24 ends the imaging for one line. Through the processing so far, image data for one line is generated.

  When the image data generation process for one line is completed once, the arithmetic processing unit 24 repeats the next image data generation process again and accumulates them for a plurality of lines to generate one image data. The image data generation process is repeatedly executed within a period from a timing immediately before the workpiece W passes the main scanning line L to a timing immediately after the workpiece W passes the main scanning line L. For this reason, the defect inspection apparatus 10 is provided with a workpiece detection sensor not shown in FIGS. 1 and 2, and one workpiece W is placed immediately before the main scanning line L (upstream position in the transport direction) by the workpiece detection sensor. When the arrival is detected, the arithmetic processing unit 24 starts the image data process. When the defect inspection apparatus 10 detects that the same workpiece W has passed immediately before the main scanning line L by the workpiece detection sensor, the arithmetic processing unit 24 ends the repetition of the image data generation process after a certain period of time.

[Defect inspection method]
In the present embodiment, a surface defect of the workpiece W can be inspected by the arithmetic processing unit 24 using the image obtained through the above image data generation process. Hereinafter, a defect inspection method that can be performed using the defect inspection apparatus 10 of the present embodiment will be described.

  FIG. 4 is a flowchart showing a procedure example of a defect inspection process executed in the arithmetic processing unit 24. Hereinafter, the outline of the defect inspection process will be described step by step.

  Step S10: First, the defect detection unit 36 acquires image data from the data storage unit 34 (data capture). The image data acquired at this time is, for example, for all two-dimensional pixels obtained by imaging the entire surface of the workpiece W.

  Step S12: Next, the defect detection unit 36 sets a defect type to be handled in the current process. The defect types are defined in advance from defect types 1 to n depending on, for example, the size and shape of the defect. For example, some of the defects have vertical stripes that are long in the vertical direction of the image, horizontal stripes that are long in the horizontal direction, and other types of defects that are spread evenly in the vertical and horizontal directions. Alternatively, there are defects that occupy a relatively wide area in the image and defects that occupy only a very narrow area. For this reason, in the present embodiment, defect types 1 to n are set in advance according to the rough defect type, and processing according to the type of defect is performed. The difference in processing for each defect type will be described later.

  Step S14: Once the current defect type (any one of 1 to n) is set in the previous step S12, the defect detection unit 36 next executes a defect detection process. In this process, the defect detection unit 36 performs a more detailed process to determine whether there is a defect. A specific processing procedure will be described later using another flowchart.

  Step S16: After returning from the defect detection process, the defect detection unit 36 confirms whether or not the inspection up to the defect type n has been completed. As a result, if the inspection up to the defect type n has not been completed yet (No), the defect detection unit 36 returns to step S12 to set the next defect type (previous + 1), and a defect detection process (step S14) again. Execute.

  When it is confirmed through the above procedure that inspection of defect types 1 to n has been completed for one image data (step S16: Yes), the defect detection unit 36 executes the next step S18.

  Step S18: The defect detection unit 36 collects the inspection results accumulated in the data storage unit 34 through the processes so far, and outputs them in a list format, for example. The list of inspection results can be represented, for example, by specifying the position (coordinates) of the defect in the image for each defect type, or by partially cutting out the defect image and making it a thumbnail.

  When the above procedure is executed, the defect detection unit 36 ends the defect inspection process for one image data. The defect detection unit 36 executes the process of FIG. 4 again when inspecting another workpiece W.

[Defect detection processing]
Next, FIG. 5 is a flowchart showing a procedure example of the defect detection process (step S14 in FIG. 4) executed in the defect inspection process. Hereinafter, the defect detection process will be described step by step.

  Step S140: The defect detection unit 36 divides the image data into a plurality of blocks according to the defect type (1 to n) to be handled this time. For example, when the image data includes X pixels in one line and is composed of Y rows in the sub-scanning direction, these X × Y pixels are roughly divided into blocks of an x × y array (matrix). Here, x and y are values smaller than X and Y, respectively (for example, about 1/4 to 1/10).

[Block division example]
Here, FIG. 6 is a schematic diagram illustrating an example in which one image data is divided into a plurality of (x × y) blocks. In FIG. 6, the entire outer frame corresponds to the boundary of the image data, and the frame lines formed in a lattice shape therein correspond to the boundary of each block. Further, in FIG. 5, the block located at the upper right corner of the entire image data is shown by dividing individual pixels included therein.

  By dividing the image data as described above, for example, at the uppermost position, the blocks B (1,1), B (2,1), B (3,1),..., B (x , 1) is generated, and at the left end position, the uppermost block B (1, 1) is followed by blocks B (1, 2), B (1, 3),. y) is generated. Therefore, an arbitrary block B (m, n) among them is positioned, for example, in the horizontal direction mth from the left and in the vertical direction nth from the top.

  In the division example shown in FIG. 6, the number of pixels is different between the vertical direction and the horizontal direction of each block. The aspect ratio (a: b) at this time can be arbitrarily set according to the defect type to be handled as described above. For example, when dealing with a defect that appears long in the vertical direction, a relatively long aspect ratio (a <b) can be employed as shown in FIG.

[See Figure 5: Defect Detection Processing]
Step S142: After the block division, the defect detection unit 36 compares the densities between adjacent blocks. For example, when an arbitrary target block B (m, n) is set, the density change amount is calculated for two neighboring blocks B (m−1, n) and B (m + 1, n) adjacent to this in the array. . Further, the density of an image viewed in each block can be expressed by, for example, an average density value of all pixels included in the block.

[Method for calculating concentration change]
Here, FIG. 7 is an explanatory diagram for explaining a calculation method of the density change amount. As illustrated, density comparison between blocks is performed in four directions for one target block B (m, n). Specifically, there are the following four types.

(1) Comparison in the horizontal direction For any target block B (m, n), specify two blocks B (m-1, n) and B (m + 1, n) adjacent to the horizontal direction A1 in the array. To do. Then, as the first change amount, the change amount of the density value from the neighboring block B (m−1, n) located on the left side to the target block B (m, n) is calculated. As the second change amount, the change amount of the density value from the target block B (m, n) to the neighboring block B (m + 1, n) located on the right side is calculated.

(2) Comparison 1 in an oblique direction
For an arbitrary block of interest B (m, n), two blocks B (m−1, n + 1) and B (m + 1, n−1) adjacent in the diagonal direction A2 in the array are specified. Then, as the first change amount, the change amount of the density value from the peripheral block B (m−1, n + 1) located diagonally lower left to the target block B (m, n) is calculated. Further, as the second change amount, the change amount of the density value from the target block B (m, n) to the peripheral block B (m + 1, n−1) located diagonally upward to the right is calculated.

(3) Comparison in diagonal direction 2
For an arbitrary block of interest B (m, n), two blocks B (m−1, n−1) and B (m + 1, n + 1) adjacent to the diagonal direction A3 in an array different from the above are specified. . Then, as the first change amount, the change amount of the density value from the peripheral block B (m−1, n−1) located on the upper left to the target block B (m, n) is calculated. Further, as the second change amount, the change amount of the density value from the target block B (m, n) to the peripheral block B (m + 1, n + 1) located diagonally lower right is calculated.

(4) Comparison in the vertical direction For any target block B (m, n), specify two blocks B (m, n-1) and B (m, n + 1) adjacent in the vertical direction A4 in the array To do. Then, the amount of change in the density value from the peripheral block B (m, n−1) located in the upper stage to the target block B (m, n) is calculated as the first amount of change. Further, as the second change amount, the change amount of the density value from the target block B (m, n) to the peripheral block B (m, n + 1) located at the lower stage is calculated.

  When the densities are compared for the above four patterns and the first change amount and the change amount of stage 2 are calculated for each direction, the defect detection unit 36 temporarily stores these calculation results.

[See Figure 5: Defect Detection Processing]
Step S144: Next, the defect detection unit 36 determines the defect condition for the first change amount and the change amount of the stage 2 for each direction. The defect condition here defines whether or not the amount of change in the density value obtained for each direction is compatible with an element for determining the presence or absence of a defect. Specifically, the defect condition is that the first change amount is a negative value (<0) and the second change amount is a positive value (> 0). The defect detection unit 36 discards the calculation result for the first and second change amounts obtained for each direction that do not satisfy the defect condition. Therefore, after this, only the calculation result that matches the defect condition is used.

  Step S146: After narrowing down the calculation result, the defect detection unit 36 calculates the defect degree from the remaining change amount. Specifically, the difference between the first change amount and the second change amount obtained for each direction (only when the conditions are met) is obtained, and the result is set as the defect degree for each direction. Since the first change amount is a negative value and the second change amount is a positive value as described above, the degree of defect can be expressed as a sum of absolute values of the change amounts. The defect detection unit 36 finally determines the maximum defect degree obtained for each direction as the defect degree (representative value) of the block of interest.

  Step S148: Next, the defect detection unit 36 compares the above-described defect degree (representative value) with a predetermined threshold value, for example, and determines the presence or absence of a defect based on the result. In addition, what is necessary is just to set the value calculated | required by experiment beforehand for every defect kind as a threshold value.

  Step S150: If a defect is detected at this time (Yes), the defect detector 36 next executes Step S152. On the other hand, if no defect is detected (No), the defect detection unit 36 proceeds to step S154.

  Step S152: When a defect is detected, the defect detector 36 calculates the unevenness (depth or height) of the defect. A specific calculation example will be described later.

  Step S154: Then, the defect detection unit 36 confirms whether or not all the divided blocks have been inspected. At this time, if the inspection has not been completed for all the blocks (No), the defect detection unit 36 proceeds to step S156.

  Step S156: The defect detection unit 36 changes the target block. For example, the position of the target block set in the current examination is changed, and the block at the new position is set as the next target block. At this time, the defect detection unit 36 can shift the positions of the blocks in the order of arrangement, for example, or can shift the positions of the blocks in a random order. However, what is once set as the block of interest is not set as the block of interest twice.

  When the block of interest is changed, the defect detection unit 36 returns to step S142 and repeats the subsequent steps (steps S142 to S154). When it is confirmed that the inspection has been completed for all the blocks (step S154: Yes), the defect detection unit 36 returns to the defect inspection process and executes the next procedure (step S16 in FIG. 4). .

  As described above, according to the defect inspection method performed using the defect inspection apparatus 10, instead of inspecting the pixels obtained by imaging the surface of the workpiece W one by one, the average density value obtained by dividing these into blocks. The number of pixels can be reduced in a pseudo manner by using the inspection. For this reason, it is possible to suppress the influence of noise other than the defects included in the image of the workpiece W having a rough surface state rather than a smooth surface and perform a more accurate defect inspection.

  Further, since the aspect ratio of the block can be set finely according to the defect type, for example, inspection can be performed focusing on defects having low contrast and defects having features in each direction on the image.

[Preventing misrecognition due to defect conditions]
In the present embodiment, since the defect condition is determined in the defect detection process as described above (step S144 in FIG. 5), it is possible to prevent erroneous recognition of the defect. Hereinafter, this point will be specifically described.

  FIG. 8 is a diagram showing a simplified example of an image of the entire image data and a density difference that appears when the image data is divided into blocks.

  In FIG. 8, (A): For example, it is assumed that one image data includes a surface area A1 corresponding to the surface of the workpiece W and other peripheral areas A0. Such image data appears prominently when a card-shaped workpiece W with four corners finished in an arc shape is imaged.

  In FIG. 8, (B): At this time, if the block of interest BL2 is set in the peripheral area A0 located outside the left end of the workpiece W, for example, when the density is compared in the left-right direction, the peripheral block BL1 located adjacent to the left There is almost no change in density from to the target block BL2. This is because these blocks BL1 and BL2 both contain many pixels in the peripheral area A0, and therefore both have a low density value (the image is black). That is, the first change amount here is zero.

  On the other hand, the peripheral block BL3 located to the right of the target block BL2 includes a large number of pixels in the surface area A1, and therefore has a relatively high density value. In this case, the density change amount (second change amount) from the target block BL2 to the peripheral block BL3 is a positive value. However, since the first change amount is 0 as described above, the defect condition is satisfied. I understand that there is no. Therefore, since the first and second change amounts are discarded without calculating the degree of defect for this target block, even if the average density value of the target block BL2 is low, this is erroneously recognized as a defect. There is no end to it.

  In FIG. 8, (C): Similarly, when the target block BR2 is set in the peripheral area A0 located outside the right end of the work W, for example, when comparing the density in the left-right direction, the target block BR2 is adjacent to the right. There is almost no density change amount to the peripheral block BR3 located, and the first change amount is zero.

  On the other hand, the peripheral block BR1 located to the left of the target block BR2 includes a large number of pixels in the surface area A1, and therefore has a relatively high density value. In this case, the density change amount (second change amount) from the peripheral block BR1 to the target block BR2 is a positive value, but since the first change amount is 0, the defect condition is not satisfied. Therefore, since the first and second change amounts are discarded without calculating the degree of defect for this target block, even if the average density value of the target block BR2 is low, this is erroneously recognized as a defect. There is no end to it.

  Therefore, although it is usually necessary to perform defect inspection except for the range of the four corners, in the present embodiment, the whole including the peripheral area A0 can be mechanically divided into blocks for inspection. For this reason, in this embodiment, it is possible to advance the defect detection process at an early stage without detecting the edge from the image data and extracting only the surface area A1 of the workpiece W from the edge.

  For example, when a defect actually exists near the left end of the workpiece W and a target block is set in the defective portion, the amount of change is discarded in the comparison in the left-right direction. Although not calculated, since the defect condition is met in the comparison in the vertical direction, the defect degree can be calculated there. Therefore, the defect near the end of the workpiece W does not leak from the detection.

[Unevenness calculation method]
Next, an unevenness calculation method performed when a defect is detected in the defect detection process will be described.

[For concave defects]
FIG. 9 is a conceptual diagram illustrating the depth calculation principle regarding the concave defect. As described above, here, the slit 50 is arranged in the light irradiation direction of the lighting fixture 22, and the slit 50 is provided at a position where the light irradiated from the lighting fixture 22 is partially blocked (about 50%). It shall be.

  On the other hand, the line sensor 18 is adjusted to a position where the light from the luminaire 22 is irradiated within 50% of the main scanning line L (imaging line) in the sub-scanning direction. Specifically, the line sensor 18 is moved in the sub-scanning direction from the position where the density value when the surface of the workpiece W is imaged using the specularly reflected light from the lighting fixture 22 to the position where the density value is 50%. Move it in parallel to adjust. Therefore, here, when there is no uneven defect on the surface of the workpiece W, the density value at the time of imaging by the line sensor 18 is 50% of the maximum value (dark field).

  By performing the above adjustment, the “light source region” in the figure corresponds to the entire irradiation range from the luminaire 22 as viewed in the sub-scanning direction, and 50% of the “dark region” in the figure is formed by the slit 50. This corresponds to a range where light is shielded, and the other “bright area” corresponds to a range where light is actually irradiated onto the workpiece W.

  Then, on the main scanning line L of the line sensor 18, an initial dark region (reference symbol S in the figure) corresponding to 50% is formed in the total light receiving range length (reference symbol X in the figure) seen in the sub-scanning direction. . The length of the initial dark area S corresponds to the length of the dark area that is shielded by the slit 50 at the surface position of the workpiece W. Therefore, normally, if there are no irregularities on the surface of the workpiece W, the main scanning line L is formed with 50% each of a region without reflected light and a region with reflected light.

  However, in the process of actually transporting the workpiece W, for example, when a concave defect (reference symbol C in the figure) on the surface thereof is positioned on the main scanning line L, it is normally reflected on the surface of the workpiece W. A change occurs in the light (50%). Specifically, it can be seen that, at the position shown in FIG. 9, the bottom of the concave defect C is irradiated with light, so that a step is generated at the position of the reflecting surface by the depth D. Such a step shifts the position of the initial dark region S with respect to the main scanning line L of the line sensor 18 in the sub-scanning direction (in the direction of the white arrow in the figure), and the dark region as a whole in the sub-scanning direction. Expanding.

  As a result, in the entire light receiving region of the line sensor 18, the region where there is no reflected light is larger than the region where the reflected light is present, and the density value of the image to be captured is correspondingly reduced. Therefore, the defect detection unit 36 can determine a portion where the density value of the image is reduced in one line as a concave defect.

[For convex defects]
FIG. 10 is a conceptual diagram showing the calculation principle of the height related to the convex defect. As is clear from the comparison with FIG. 9, for the convex defect (symbol P in the figure), the region with the reflected light is smaller than the region without the reflected light, and the density of the image to be captured is correspondingly reduced. Since the value becomes high, a portion where the density value of the image in one line is high can be determined as a convex defect.

  The inventor of the present invention pays attention to the fact that the density value of the image changes due to the presence of the concavo-convex defect as described above, and found a method for calculating the depth of the concave defect or the height of the convex defect by the following method. .

Various variables used in the calculation are shown below.
(1) Light source incident angle: α
(2) Reflection angle (incident angle to the line sensor 18) α
(3) Concave defect depth: D
Convex defect height: H
(4) Image density value when the slit 50 is not used (when all the reflected light in the light source region is received by the line sensor 18): Imax
(5) Total light receiving range length in the sub-scanning direction of the line sensor 18: X
(6) Initial dark region length by slit 50: S

First, the density value Ia when the surface of the workpiece W having no defect is imaged is expressed by the following formula (A).
Ia = (1-S / X) · Imax (A)
From the above formula (A), it is understood that when there is no defect on the surface of the workpiece W and the initial dark region length S is 50% of the total light receiving range length X, the density value Ia is 50% of Imax. .

Further, the density value Ib when imaging the concave defect having the depth D is expressed by the following formula (B1).
Ib = (1− (2D · tan α + S) / X) · Imax (B1)

From the above equations (A) and (B1), the depth D of the concave defect can be calculated using the following equation (C1).
D = (1-Ia / Ib). (X-S). (1/2 tan.alpha.) (C1)

On the other hand, the density value Ib when a convex defect having a height H is imaged is expressed by the following equation (B2).
Ib = (1− (S−2H · tan α) / X) · Imax (B2)

From the above formulas (A) and (B2), the height H of the convex defect can be calculated using the following formula (C2).
H =-(1-Ib / Ia). (X-S). (1/2 tan.alpha.) (C2)

  As described above, in the defect detection method applied to the present embodiment, distortion occurs in the shadow portion in the dark field by the line sensor 18 in the process in which the concave defect or the convex defect passes on the main scanning line L. By utilizing this, it is possible to detect the uneven defect present on the surface of the workpiece W and to easily calculate the depth or height thereof.

  Moreover, since both a concave defect and a convex defect can be detected simultaneously by one type of illumination method using the lighting fixture 22, even if the concave defect and the convex defect are mixed on the surface of the workpiece W, these Can be detected simultaneously, and the two can be clearly distinguished and recognized.

[Other Embodiments]
The present invention can be implemented with various modifications without being limited to the above-described embodiment. In one embodiment, the workpiece W is conveyed and imaged in the sub-scanning direction. However, the workpiece W is fixed, and on the contrary, the main scanning line L is imaged while moving in the sub-scanning direction with respect to the workpiece W. Also good.

  In one embodiment, grayscale image data is handled, but the image data may be processed as color.

  In addition, the arrangement of various devices described in the embodiment is a preferable example, and it is needless to say that the arrangement of the devices can be appropriately changed when the present invention is implemented.

It is the figure which showed schematically the example of a structure of the defect inspection apparatus. It is the perspective view which showed the process in which a workpiece | work is imaged with a line sensor. It is a block diagram which shows the structure of an arithmetic processing part in detail. It is the flowchart which showed the example of the procedure of the defect inspection process. It is a flowchart which shows the example of a procedure of the defect detection process performed in a defect inspection process. It is the schematic which shows the example at the time of dividing | segmenting one image data into a some block. It is explanatory drawing for demonstrating the calculation method of density variation. It is the figure which simplified and showed the example of the density difference which appears when the image of the whole image data and this are divided | segmented into a block. It is a conceptual diagram which shows the calculation principle of the depth regarding a concave defect. It is a conceptual diagram which shows the calculation principle of the height regarding a convex defect.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Defect inspection apparatus 12 Belt conveyor 14 Rotary encoder 18 Line sensor 20 Lens 22 Lighting fixture 24 Arithmetic processing part 30 Input part 32 Data processing part 34 Data storage part 36 Defect detection part 38 Output part 40 CPU

Claims (6)

An acquisition step of acquiring an image of the inspected object;
A dividing step of dividing the acquired image into a plurality of blocks in a predetermined arrangement;
A target block to be inspected among the plurality of divided blocks is set, and the density of the image viewed in the target block and the density of the image viewed in a peripheral block adjacent to the target block are set in the array. A comparison step of comparing each of the two neighboring blocks adjacent to each other in one direction,
As a result of the comparison, only when the density in the target block is lower than the density in the two neighboring blocks, the density in the target block and the density in each peripheral block are A calculation step for obtaining the degree of defect of the block of interest by adding together the two amounts of change obtained between,
And a determination step of determining whether or not there is a defect in the block of interest based on the obtained defect degree. The defect inspection method according to claim 1,
A defect inspection method characterized by executing the comparison step, the calculation step, and the determination step for each block by sequentially setting the plurality of divided blocks as the block of interest. In the defect inspection method according to claim 1 or 2,
In the comparison step,
Of the two peripheral blocks, a difference in density in the target block with respect to density in one of the peripheral blocks is set as a first change amount, and in the other peripheral block with respect to the density in the target block. The difference in density of each is determined as the second change amount,
In the calculation step,
Only when the first change amount has a negative value and the second change amount has a positive value, the difference between the first change amount and the second change amount is determined as the defect. Defect inspection method characterized in that it is a degree. In the defect inspection method in any one of Claim 1 to 3,
In the comparison step,
For one target block, a set of two neighboring blocks adjacent to the target block is defined in a plurality of directions within the array to compare the density in a plurality of ways for each direction,
In the calculation step,
For each direction in which the comparison is performed, two change amounts obtained between the density in the block of interest and the density in each of the peripheral blocks are added together, and the change amount that is the maximum value among them is calculated as A defect inspection method characterized by setting a defect degree of a block of interest. The defect inspection method according to any one of claims 1 to 4,
In the dividing step,
A defect inspection method characterized by changing the shape of a block into which an image is divided according to the type of defect to be detected. Image generating means for capturing an image of the inspection object and generating an image;
The image generated by the image generation unit is divided into a plurality of blocks in a predetermined arrangement, and the image density viewed in the target block set as the inspection target and the image viewed in the peripheral block adjacent to the target block A comparison means for comparing each of the peripheral blocks adjacent to each other in one direction in the array,
As a result of comparison by the comparison means, only when the density in the target block is lower than the density in the two peripheral blocks, the density in the target block and A calculating means for calculating the degree of defect of the block of interest by adding together the two amounts of change calculated with respect to the density;
A defect inspection apparatus comprising: a determination unit that determines the presence / absence of a defect in the target block based on the defect degree obtained by the calculation unit.

Images