CN108961213B - Equipment for realizing batch inspection of drilling quality and detection method thereof - Google Patents
Equipment for realizing batch inspection of drilling quality and detection method thereof Download PDFInfo
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- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
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- G06T2207/20212—Image combination
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- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30141—Printed circuit board [PCB]
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Abstract
The invention discloses equipment for realizing batch inspection of drilling quality, which comprises a plurality of conveyor belts which are arranged in series and used for conveying PCB boards, wherein a gap is reserved between any two adjacent conveyor belts; a backlight source is arranged below a gap between the conveying belts, three CIS linear array cameras are arranged above the gap between the conveying belts, one CIS linear array camera is coaxially arranged with the backlight source, the other two CIS linear array cameras are symmetrically arranged by taking the backlight source as a symmetry axis, and the included angle between the symmetrically arranged CIS linear array cameras and the backlight source is 15 degrees; the image synthesis module is used for synthesizing images acquired by different CIS linear array cameras; the image identification module is used for comparing and identifying the synthesized image with a prestored image; and the database module stores pre-stored images for comparison and identification. The invention can improve the defects of the prior art and improve the efficiency of detecting the drilling of the PCB.
Description
Technical Field
The invention relates to the field of automatic visual detection, in particular to equipment for realizing batch detection of drilling quality and a detection method thereof.
Background
With the development of integrated circuit technology, the manufacturing requirements of the PCB board are higher and higher. This puts higher demands on the quality inspection of the PCB board. In order to manufacture a specific circuit on the PCB, the surface of the PCB needs to be drilled, how to quickly detect the drilled hole on the PCB, and the quality problems of hole size, hole plug, hole deformation and the like can be found in time, so that the key problem of improving the production line efficiency of the PCB is solved.
Disclosure of Invention
The invention aims to provide equipment for realizing batch inspection of drilling quality and a detection method thereof, which can overcome the defects of the prior art and improve the efficiency of PCB drilling detection.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
An apparatus for realizing batch inspection of drilling quality comprises,
the conveying belts are arranged in series and used for conveying the PCB, and a gap is reserved between any two adjacent conveying belts;
a backlight source is arranged below a gap between the conveying belts, three CIS linear array cameras are arranged above the gap between the conveying belts, one CIS linear array camera is coaxially arranged with the backlight source, the other two CIS linear array cameras are symmetrically arranged by taking the backlight source as a symmetry axis, and the included angle between the symmetrically arranged CIS linear array cameras and the backlight source is 15 degrees;
the image synthesis module is used for synthesizing images acquired by different CIS linear array cameras;
the image identification module is used for comparing and identifying the synthesized image with a prestored image;
and the database module stores pre-stored images for comparison and identification.
The detection method of the equipment for realizing batch inspection of the drilling quality comprises the following steps:
A. the method comprises the following steps that a PCB is transmitted on a conveyor belt, and when the PCB passes through the upper part of a backlight source, three CIS linear array cameras photograph drilling positions on the PCB for image acquisition;
B. transmitting the images at the same position to an image synthesis module, and synthesizing the images to obtain a synthesized image;
C. and the image identification module compares and identifies the synthesized image with a prestored image stored in the database module to obtain an identification result.
Preferably, in the step B, the synthesizing of the image includes the steps of,
b1, establishing a mapping relation set F of a front view image shot by the CIS linear array camera and side view images shot by the other two CIS linear array cameras, which are coaxially arranged with the backlight source, according to the shooting angles of the three CIS linear array cameras;
b2, selecting a plurality of characteristic areas in the front image, calculating corresponding images of the characteristic areas in the side-view images through a mapping relation set, and comparing the corresponding images with corresponding positions of the actually shot side-view images to obtain an image deviation value matrix D;
b3, correcting the mapping relation set F by using the image deviation value matrix D, and using the corrected mapping relation set
And supplementing the side-view image into the main-view image.
Preferably, in step B3, the correction of the mapping relationship set F includes the steps of,
b31, selecting the geometric center of the characteristic region as a correction starting position,
wherein F is the mapping relation before correction,
the mapping relation after correction, k is a correction coefficient, the dimension of the correction coefficient is used for balancing the dimension of two ends of the formula, and d is an image deviation value of the geometric center;
b32, for other pixel points of the characteristic region,
wherein, F is the mapping relation before correction,
is the mapping after correction, k is the correction factor, the dimension of which is used to balance the quantities at both ends of the formulaD is an image deviation value of the pixel point to be corrected, and L is the Euclidean distance between the pixel point to be corrected and the geometric central point;
b33, for the pixel points of the non-characteristic region,
wherein F is the mapping relation before correction,
is the mapping relationship after correction, k is the correction coefficient, the dimension of which is used to balance the dimensions at both ends of the formula,
the image deviation average value of the characteristic region where the geometric center closest to the Euclidean distance of the pixel point to be corrected is located, and L is the Euclidean distance between the pixel point to be corrected and the geometric center point;
and B34, smoothing the corrected image.
Preferably, the step C of comparing the synthesized image with the pre-stored image stored in the database module for identification comprises the steps of,
c1, calling the feature points in the pre-stored images in the database module, traversing and comparing the synthesized images by using the feature points, if all the feature points are compared, switching to the step C2, otherwise, replacing the pre-stored images and re-executing the step C1;
c2, respectively connecting all different feature points in the pre-stored image and the synthesized image through straight lines to form a feature grid; if the similarity of the feature grids in the pre-stored image and the synthesized image is larger than a threshold value, and the gray scale error of the corresponding straight line segment in the feature grid is smaller than the vector error corresponding to the straight line segment where the feature grid is located, the identification is successful, otherwise, the identification is failed.
Preferably, in step C1, if the similarity between the corresponding position of the composite image and the feature point is greater than the threshold value, the comparison is determined to be successful, and the similarity is calculated by,
wherein, S is the similarity of the images,
in order to be a gray-scale-similar component,
in the form of a similar component of the shape,
and
are weight coefficients.
Adopt the beneficial effect that above-mentioned technical scheme brought to lie in: according to the invention, by establishing the multi-angle image acquisition device and optimizing the image synthesis method, the distortion of the synthesized image is reduced, the characteristic information quantity carried by the image is increased, and the image is convenient to identify. The image identification process is realized by using the characteristic points and the characteristic network, so that the operation amount is greatly reduced on the premise of ensuring the identification accuracy, and the identification speed is improved.
Drawings
FIG. 1 is a schematic diagram of one embodiment of the present invention.
In the figure: 1. a conveyor belt; 2. a PCB board; 3. a backlight source; 4. a CIS line camera; 5. an image synthesis module; 6. an image recognition module; 7. and a database module.
Detailed Description
Referring to fig. 1, one embodiment of the present invention includes,
the conveyor belts 1 are arranged in series and used for conveying the PCB 2, and a gap is reserved between any two adjacent conveyor belts 1;
a backlight source 3 is arranged below a gap between the conveying belts 1, three CIS linear cameras 4 are arranged above the gap between the conveying belts 1, one CIS linear camera 4 is coaxially arranged with the backlight source 3, the other two CIS linear cameras 4 are symmetrically arranged by taking the backlight source 3 as a symmetry axis, and the included angle between the symmetrically arranged CIS linear cameras 4 and the backlight source 3 is 15 degrees;
the image synthesis module 5 is used for synthesizing images acquired by different CIS linear cameras 4;
the image identification module 6 is used for comparing and identifying the synthesized image with a prestored image;
and the database module 7 is used for storing pre-stored images for comparison and identification.
The detection method of the equipment for realizing batch inspection of the drilling quality comprises the following steps:
A. the PCB 2 is transmitted on the conveyor belt 1, and when the PCB passes above the backlight 3, the three CIS linear array cameras 4 take pictures of drilling positions on the PCB 2 and acquire images;
B. transmitting the images at the same position to an image synthesis module 5, and synthesizing the images to obtain a synthesized image;
C. and the image identification module 6 compares and identifies the synthesized image with a prestored image stored in the database module 7 to obtain an identification result.
In the step B, the synthesizing of the image includes the steps of,
b1, establishing a mapping relation set F of a front view image shot by the CIS linear cameras 4 and side view images shot by the other two CIS linear cameras 4, wherein the front view images are coaxially arranged with the backlight 3, and the side view images are shot by the CIS linear cameras 4 according to the shooting angles of the three CIS linear cameras 4;
b2, selecting a plurality of characteristic areas in the front image, calculating corresponding images of the characteristic areas in the side-view images through a mapping relation set, and comparing the corresponding images with corresponding positions of the actually shot side-view images to obtain an image deviation value matrix D;
b3, correcting the mapping relation set F by using the image deviation value matrix D, and using the corrected mapping relation set
And supplementing the side-view image into the main-view image.
In step B3, the correction of the mapping relationship set F includes the following steps,
b31, selecting the geometric center of the characteristic region as a correction starting position,
wherein F is the mapping relation before correction,
the mapping relation after correction, k is a correction coefficient, the dimension of the correction coefficient is used for balancing the dimension of two ends of the formula, and d is an image deviation value of the geometric center;
b32, for other pixel points of the characteristic region,
wherein, F is the mapping relation before correction,
the mapping relation after correction is obtained, k is a correction coefficient, dimensions of the correction coefficient are used for balancing dimensions at two ends of a formula, d is an image deviation value of a pixel point to be corrected, and L is the Euclidean distance between the pixel point to be corrected and a geometric central point;
b33, for the pixel points of the non-characteristic region,
wherein F is the mapping relation before correction,
is the mapping relationship after correction, k is the correction coefficient, the dimension of which is used to balance the dimensions at both ends of the formula,
the image deviation average value of the characteristic region where the geometric center closest to the Euclidean distance of the pixel point to be corrected is located, and L is the Euclidean distance between the pixel point to be corrected and the geometric center point;
and B34, smoothing the corrected image.
In step C, the step of comparing and recognizing the synthesized image with the pre-stored image stored in the database module 7 comprises the following steps,
c1, calling feature points in images prestored in the database module 7, traversing and comparing the synthesized images by using the feature points, if all the feature points are compared, switching to the step C2, and otherwise, replacing the prestored images and re-executing the step C1;
c2, respectively connecting all different feature points in the pre-stored image and the synthesized image through straight lines to form a feature grid; if the similarity of the feature grids in the pre-stored image and the synthesized image is larger than a threshold value, and the gray scale error of the corresponding straight line segment in the feature grid is smaller than the vector error corresponding to the straight line segment where the feature grid is located, the identification is successful, otherwise, the identification is failed.
In step C1, if the similarity between the feature point and the corresponding position of the composite image is greater than the threshold, the comparison is determined to be successful, and the similarity is calculated by,
wherein, S is the similarity of the images,
in order to be a gray-scale-similar component,
in the form of a similar component of the shape,
and
are weight coefficients.
In addition, in step B2, the gray scale variation of the selected feature region is greater than a preset threshold, and the similarity between any two feature regions is smaller than a set threshold. In this embodiment, the various thresholds are set through field tests according to the actual situation of the image.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. A detection method of equipment for realizing batch inspection of drilling hole quality comprises the following steps,
the conveying belts (1) are arranged in series and used for conveying the PCB (2), and a gap is reserved between any two adjacent conveying belts (1);
a backlight source (3) is arranged below a gap between the conveyor belts (1), three CIS linear array cameras (4) are arranged above the gap between the conveyor belts (1), one CIS linear array camera (4) is coaxially arranged with the backlight source (3), the other two CIS linear array cameras (4) are symmetrically arranged by taking the backlight source (3) as a symmetry axis, and the included angle between the symmetrically arranged CIS linear array cameras (4) and the backlight source (3) is 15 degrees;
the image synthesis module (5) is used for synthesizing images acquired by different CIS linear cameras (4);
the image identification module (6) is used for comparing and identifying the synthesized image with a prestored image;
the database module (7) is used for storing pre-stored images for comparison and identification;
the method is characterized by comprising the following steps:
A. the PCB (2) is transmitted on the conveyor belt (1), and when passing through the upper part of the backlight source (3), the three CIS linear array cameras (4) photograph the drilling positions on the PCB (2) for image acquisition;
B. transmitting the images at the same position to an image synthesis module (5), and synthesizing the images to obtain a synthesized image;
the synthesis of the image comprises the following steps,
b1, establishing a mapping relation set F of a front view image shot by the CIS linear cameras (4) which are coaxially arranged with the backlight source (3) and side view images shot by the other two CIS linear cameras (4) according to the shooting angles of the three CIS linear cameras (4);
b2, selecting a plurality of characteristic areas in the front image, calculating corresponding images of the characteristic areas in the side-view images through a mapping relation set, and comparing the corresponding images with corresponding positions of the actually shot side-view images to obtain an image deviation value matrix D;
b3, correcting the mapping relation set F by using the image deviation value matrix D, and using the corrected mapping relation set
Supplementing the side-view image into the main-view image;
the correction of the mapping relation set F includes the following steps,
b31, selecting the geometric center of the characteristic region as a correction starting position,
wherein F is the mapping relation before correction,
the mapping relation after correction, k is a correction coefficient, the dimension of the correction coefficient is used for balancing the dimension of two ends of the formula, and d is an image deviation value of the geometric center;
b32, for other pixel points of the characteristic region,
wherein F is the mapping relation before correction,
the mapping relation after correction is obtained, k is a correction coefficient, dimensions of the correction coefficient are used for balancing dimensions at two ends of a formula, d is an image deviation value of a pixel point to be corrected, and L is the Euclidean distance between the pixel point to be corrected and a geometric central point;
b33, for the pixel points of the non-characteristic region,
wherein F is the mapping relation before correction,
is the mapping relationship after correction, k is the correction coefficient, the dimension of which is used to balance the dimensions at both ends of the formula,
is the average value of the image deviation of the characteristic region of the geometric center closest to the Euclidean distance of the pixel point to be corrected, and L is the pixel point to be corrected and the geometric centerThe Euclidean distance of the center point;
b34, smoothing the corrected image;
C. and the image recognition module (6) compares and recognizes the synthesized image with a prestored image stored in the database module (7) to obtain a recognition result.
2. The inspection method of the apparatus for realizing mass inspection of the quality of drilled holes according to claim 1, wherein: in the step C, the step of comparing and identifying the synthesized image with the prestored image stored in the database module (7) comprises the following steps,
c1, calling feature points in images prestored in the database module (7), traversing and comparing the synthesized images by using the feature points, if all the feature points are compared, switching to the step C2, and otherwise, replacing the prestored images and re-executing the step C1;
c2, respectively connecting all different feature points in the pre-stored image and the synthesized image through straight lines to form a feature grid; if the similarity of the feature grids in the pre-stored image and the synthesized image is larger than a threshold value, and the gray scale error of the corresponding straight line segment in the feature grid is smaller than the vector error corresponding to the straight line segment where the feature grid is located, the identification is successful, otherwise, the identification is failed.
3. The inspection method of the apparatus for realizing mass inspection of the quality of the drilled hole according to claim 2, wherein: in step C1, if the similarity between the feature point and the corresponding position of the composite image is greater than the threshold, the comparison is determined to be successful, and the similarity is calculated by,
wherein, S is the similarity of the images,
in order to be a gray-scale-similar component,
in the form of a similar component of the shape,
and
are weight coefficients.
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| CN111965185B (en) * | 2020-07-15 | 2023-05-02 | 镇江苏仪德科技有限公司 | PL detection device and method for different-waveband light sources |
| CN116008191A (en) * | 2021-10-21 | 2023-04-25 | 健鼎（湖北）电子有限公司 | Drilling quality detection system and drilling quality detection method |
| CN117309873B (en) * | 2023-09-04 | 2024-08-06 | 淮安特创科技有限公司 | Efficient PCB appearance detection system and method |
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