CN110648308A - Method for identifying and positioning defects of PCB (printed circuit board) lead - Google Patents
Method for identifying and positioning defects of PCB (printed circuit board) lead Download PDFInfo
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- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 2
- 229910052802 copper Inorganic materials 0.000 description 2
- 239000010949 copper Substances 0.000 description 2
- 150000003071 polychlorinated biphenyls Chemical class 0.000 description 2
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- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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Abstract
In order to solve the defects of inaccurate positioning and inaccurate defect type identification of non-contact detection equipment in the prior art, the invention provides a method for identifying and positioning defects of a PCB (printed circuit board) wire, which comprises the following steps: collecting an image; processing an image; identifying and positioning defects; and displaying the detection result. The invention is mainly applied to two image processing software of Labview and Matlab. Matlab software carries out image processing such as graying, image enhancement, binarization and the like on an image, and has easy operation and simple method realization. The processed image is matched and compared with the reference image on the Labview, so that the defect identification and positioning operation is more convenient and faster.
Description
Technical Field
The invention relates to the field of PCB defect judgment, in particular to a method for identifying and positioning PCB lead defects.
Background
In the course of the rapid development of PCBs, many problems, mainly the quality of PCBs, are faced. Various defects are generated in the process of manufacturing the printed circuit board, such as short circuit, residual copper, defect and the like of a lead, which affect the quality of the PCB. These problems can even result in the rejection of the entire circuit board if the test is not timely. As the circuit density of the designed circuit board is higher, the number of the layers is larger, and the influence of many uncertain factors such as environment, temperature, raw materials and artificial misoperation in the production process can cause defects, so that the rejection rate of the production is increased, and the cost of the product is increased. Therefore, in the production process, how to reduce the rejection rate and improve the production efficiency and how to improve the quality of the printed circuit board is a significant problem.
At present, the PCB defect detection methods at home and abroad mainly comprise contact type and non-contact type. The contact detection method plays an important role in the early development stage of the PCB. Contact detection devices today mainly have two types, needle bed and flying needle. The neilsbed type tester is suitable for detecting on large-batch medium and low density PCB boards, and the detection speed is high. If a needle bed type tester is used in the manufacturing detection, whether a problem exists can be diagnosed. However, the existing needle bed type tester has many defects: different templates are manufactured according to different circuit boards, the manufacturing and debugging period of the templates is long, the cost is high, the templates are not suitable for testing a low-yield prototype, the testing coverage is limited, the defects of short circuit and open circuit can be tested, and shielded assembly cannot be tested. In contrast, flying probe testers are mainly used in test systems for small-lot, multi-variety product production, and use a flexible moving probe to test the electrical performance of a whole circuit board. The flying probe detection also has the defects of long test time because the detection method is point-by-point detection; but also small pits may be left in the solder at the vias and pads. The probe contacts the component pin at a position without the bonding pad, so that the component pin which is loosened or poorly welded may be missed; the probe tester also limits the size of the circuit board.
In the non-contact detection equipment, the detection sensor does not directly and physically contact with the detected object, so that the defects of the PCB can be detected, and the faults caused by physical contact can be avoided.
However, the existing detection technology has some disadvantages, such as inaccurate positioning, complex operation, wrong category identification, and the like.
Disclosure of Invention
In order to solve the defects of inaccurate positioning and inaccurate defect type identification of non-contact detection equipment in the prior art, the invention provides a method for identifying and positioning defects of a PCB (printed circuit board) wire.
In order to achieve the purpose, the invention adopts the specific scheme that: a method for identifying and positioning defects of a PCB (printed circuit board) lead is characterized by comprising the following steps:
s1, setting a standard PCB and a standard binary image corresponding to the standard PCB;
s2, collecting an image of the PCB to be detected;
s3, carrying out gray level processing on the image of the PCB to be detected obtained in the step S2, and then obtaining a binary image of the PCB to be detected through threshold segmentation;
s4, performing exclusive OR operation on the binary image of the PCB to be detected obtained in the S3 and the standard binary image obtained in the S1 step to obtain an exclusive OR operation result;
the formula of the exclusive or operation is: d (i, j) ═ FStandard of merit(i，j)XOR FTo be measured(i, j); wherein, FStandard of meritIs the pixel value of (i, j) of the point on the standard binary image in the step S1, FTo be measured(i, j) is the pixel value of a point (i, j) on the image to be detected, and D (i, j) is the result of XOR operation of the standard binary image and the image to be detected at the point (i, j); judging whether the PCB to be detected has defects or not according to the result of the XOR operation;
and S5, if the PCB to be detected has defects as a result of the step S4, finding the positions and the types of the defects in Labview software.
The whole processing procedure in the step S3 is:
s301, firstly, carrying out gray level processing; : on Matlab software, the self-contained function inside Matlab is adopted: rgb2gray, formula: f (i, j) ═ 0.30 × R (i, j) +0.59 × G (i, j) +0.11 × B (i, j);
where f (i, j) represents the grayscale value of the converted grayscale image at (i, j), R, G, B representing the red, green, and blue color components, respectively; wherein R (i, j) is a red value at the position (i, j) in the PCB to be detected; g (i, j) is a green value at (i, j) in the PCB to be detected and B (i, j) is a blue value at (i, j) in the PCB to be detected;
s302, enhancing the contrast of the image: carrying out gray level histogram transformation, carrying out image smoothing and noise elimination by using mean value filtering and median filtering, using a self-contained function in Matlab, and calling the function formats as follows:
h＝ones(5，5)/25；F＝imfilter(I，h)；
J＝medfilt2(I，[5，5])；
s303, performing threshold segmentation by using an OTSU method to obtain a binary image of the PCB to be detected, which has clear characteristics and low noise;
and S304, calculating a global gray threshold value of the binary image of the PCB to be detected obtained in the step S303 by utilizing a large law algorithm to perform denoising processing.
Has the advantages that: the invention is mainly applied to two image processing software of Labview and Matlab.
Matlab software carries out image processing such as graying, image enhancement, binarization and the like on an image, and has easy operation and simple method realization. The processed image is matched and compared with the reference image on the Labview, so that the defect identification and positioning operation is more convenient and faster.
Drawings
FIG. 1 is a block diagram of the steps of the present invention.
Fig. 2 is a flow chart of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The method uses Matlab and Labview software to identify and locate the defects of the PCB wires. As shown in fig. 1, the main steps are: collecting an image; processing an image; identifying and positioning defects; and displaying the detection result.
As shown in fig. 2, before the detection and the judgment, a standard PCB and a standard binary image corresponding to the standard PCB are set.
Then, in the aspect of image acquisition, in order to obtain a high-quality image, the shooting environment is adjusted well, the acquired image is uniformly received, and the difficulty of subsequent image processing is reduced.
Then, the image processing includes: graying processing, image enhancement, threshold segmentation, and the like. And (3) calling Matlab software by Labview software to select an image preprocessing method according to the characteristics of the PCB to obtain the PCB image convenient to identify. On Matlab software, the invention firstly carries out gray processing on the collected image, and adopts a function of the interior self-carrying of Mathlab: rgb2gray, formula:
f(i,j)＝0.30*R(i,j)+0.59*G(i,j)+0.11*B(i,j)
where f (i, j) represents the gray value of the converted gray-scale image at (i, j), and R, G, B represents the three color components.
And then carrying out gray level histogram transformation on the image to enhance the contrast of the image. Using mean filtering and median filtering to smooth and eliminate noise of the image, using a self-contained function in Matlab, and calling the function formats as follows:
h＝ones(5，5)/25；F＝imfilter(I，h)；
J＝medfilt2(I，[5，5])；
and then, performing threshold segmentation by using an OTSU method to obtain a PCB binary image with clear characteristics and low noise. And denoising the image according to the processed image. Directly calling a graythresh function provided by Matlab, and calculating a global gray threshold by using a large law algorithm, wherein the calling format is as follows:
LEVEL＝graythresh(I)；
and finding out the specific position of the defect in the image processed by the Matlab software through matching of Labview and a reference image and XOR operation, and analyzing and judging the type of the defect.
The method comprises the following specific steps: image matching, image comparison, defect position coordinate determination and type judgment.
The image matching uses a template matching function IMAQ Match Pattern 2.Vi in IMAQ VISION on Labview software, and can monitor and calculate the inclination angle, the scaling ratio, the similarity and the like of a central target and a relative template of a target area.
Specifically, the patent uses an image exclusive or algorithm in image comparison, and puts the binary images obtained by the above series of processing on the reference image and the defect image together for exclusive or operation. Exclusive-or formula:
D(i，j)＝Fstandard of merit(i，j)XOR FTo be measured(i，j)；
Wherein, FStandard of meritPixel value of (i, j) which is a point on the standard image, FTo be measuredAnd (i, j) is the pixel value of a point (i, j) on the image to be measured, and D (i, j) is the result of carrying out exclusive OR operation on the point (i, j) of the standard image and the image to be measured.
We can judge the existence of the defect by the xor operation. When a defect exists, the defect needs to be processed, and the specific position of the defect needs to be determined. Using a function module IMAQ Particle Analysis report in an image Analysis module to perform Particle point Analysis report in the image, and displaying detailed information of the defects: the size of the area of the defect, the center coordinates, etc. And finally, judging the defect type by using an IMAQ Particle analysis.Vi function in NI VISION.
Through the series of operations, a display interface is finally established in Labview software, and the detection result is displayed.
In a word, the method is mainly applied to two image processing software, namely Labview and Matlab. Matlab software carries out image processing such as graying, image enhancement, binarization and the like on an image, and has easy operation and simple method realization. The processed image is matched and compared with a reference image on a Labview, the Labview is a high-efficiency graphical virtual instrument development platform, maintenance and expansion are easy, graphical operation vision is more clear, the process looks clearer, and therefore identification and defect positioning operation is more convenient and faster. In the analysis and determination of an image, an image processing method such as mathematical morphological operation is also used. The invention analyzes and judges the types of the defects such as short circuit, open circuit, pits, bulges, pores, residual copper and the like by using methods such as communicated region labeling and the like, and displays the specific positions of the defects in the image.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily change or replace the present invention within the technical scope of the present invention. Therefore, the protection scope of the present invention is subject to the protection scope of the claims.
Claims (2)
1. A method for identifying and positioning defects of a PCB (printed circuit board) lead is characterized by comprising the following steps:
s1, setting a standard PCB and a standard binary image corresponding to the standard PCB;
s2, collecting an image of the PCB to be detected;
s3, carrying out gray level processing on the image of the PCB to be detected obtained in the step S2, and then obtaining a binary image of the PCB to be detected through threshold segmentation;
s4, performing exclusive OR operation on the binary image of the PCB to be detected obtained in the S3 and the standard binary image obtained in the S1 step to obtain an exclusive OR operation result;
the formula of the exclusive or operation is: d (i, j) ═ FStandard of merit(i，j)XORFTo be measured(i, j); wherein, FStandard of meritIs the pixel value of (i, j) of the point on the standard binary image in the step S1, FTo be measured(i, j) is the pixel value of a point (i, j) on the image to be detected, and D (i, j) is the result of XOR operation of the standard binary image and the image to be detected at the point (i, j);
and S5, if the PCB to be detected has defects as a result of the step S4, finding the positions and the types of the defects in Labview software.
2. The method for identifying and locating the PCB lead defects according to claim 1, wherein the whole processing procedure in the step S3 is as follows:
s301, firstly, carrying out gray level processing; : on Matlab software, the self-contained function inside Matlab is adopted: rgb2gray, formula: f (i, j) ═ 0.30 × R (i, j) +0.59 × G (i, j) +0.11 × B (i, j);
where f (i, j) represents the grayscale value of the converted grayscale image at (i, j), R, G, B representing the red, green, and blue color components, respectively; wherein R (i, j) is a red value at the position (i, j) in the PCB to be detected; g (i, j) is a green value at (i, j) in the PCB to be detected and B (i, j) is a blue value at (i, j) in the PCB to be detected;
s302, enhancing the contrast of the image: carrying out gray level histogram transformation, carrying out image smoothing and noise elimination by using mean value filtering and median filtering, using a self-contained function in Matlab, and calling the function formats as follows:
h＝ones(5,5)/25；F＝imfilter(I,h)；
J＝medfilt2(I,[5,5])；
s303, performing threshold segmentation by using an OTSU method to obtain a binary image of the PCB to be detected, which has clear characteristics and low noise;
and S304, calculating a global gray threshold value of the binary image of the PCB to be detected obtained in the step S303 by utilizing a large law algorithm to perform denoising processing.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111798443A (en) * | 2020-07-16 | 2020-10-20 | 佛山市南海区广工大数控装备协同创新研究院 | Method for positioning and visualizing defects by utilizing PCB defect detection system |
| CN114216915A (en) * | 2021-12-15 | 2022-03-22 | 江门市浩远科技有限公司 | Method for detecting and classifying types of stains and defects of circuit board based on class level |
| CN114842275A (en) * | 2022-07-06 | 2022-08-02 | 成都数之联科技股份有限公司 | Circuit board defect judging method, training method, device, equipment and storage medium |
| CN115018828A (en) * | 2022-08-03 | 2022-09-06 | 深圳市尹泰明电子有限公司 | Defect detection method for electronic component |
| CN117372434A (en) * | 2023-12-08 | 2024-01-09 | 深圳市强达电路股份有限公司 | Positioning system and method for PCB production |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111798443A (en) * | 2020-07-16 | 2020-10-20 | 佛山市南海区广工大数控装备协同创新研究院 | Method for positioning and visualizing defects by utilizing PCB defect detection system |
| CN114216915A (en) * | 2021-12-15 | 2022-03-22 | 江门市浩远科技有限公司 | Method for detecting and classifying types of stains and defects of circuit board based on class level |
| CN114216915B (en) * | 2021-12-15 | 2024-03-29 | 江门市浩远科技有限公司 | Method for detecting and classifying class levels based on stains and defect types of circuit board |
| CN114842275A (en) * | 2022-07-06 | 2022-08-02 | 成都数之联科技股份有限公司 | Circuit board defect judging method, training method, device, equipment and storage medium |
| CN115018828A (en) * | 2022-08-03 | 2022-09-06 | 深圳市尹泰明电子有限公司 | Defect detection method for electronic component |
| CN117372434A (en) * | 2023-12-08 | 2024-01-09 | 深圳市强达电路股份有限公司 | Positioning system and method for PCB production |
| CN117372434B (en) * | 2023-12-08 | 2024-04-30 | 深圳市强达电路股份有限公司 | Positioning system and method for PCB production |
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