Method and system for monitoring copper deposition and electroplating quality of PCB (Printed Circuit Board) based on video image analysis

Abstract

The invention discloses a PCB copper deposition electroplating quality monitoring method and system based on video image analysis, and relates to the technical field of quality monitoring, through multi-view shooting and an image splicing technology, the method can obtain PCB surface images before and after electroplating in multiple aspects, ensures that the quality monitoring of each area can be covered, and improves the quality monitoring efficiency. And the blind area problem caused by view angle limitation in the traditional detection method is further avoided. The gray difference value is accurately extracted by using an image processing technology, so that possible quality defects in the electroplating process can be efficiently and accurately analyzed, and particularly, tiny uneven electroplating or local electroplating defects can be efficiently and accurately analyzed. According to the method, the non-conductive area and the conductive area on the surface of the PCB are automatically identified in combination with the PCB design drawing, and the PCB surface area division threshold W is set on the basis, so that the electroplated pseudo-conductive area can be effectively identified, and errors caused by manual intervention are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of quality monitoring, and in particular to a PCB copper electroplating quality monitoring method and system based on video image analysis. Background Art

[0002] PCB (Printed Circuit Board) is an important basic component of modern electronic products and is widely used in industries such as consumer electronics, communication equipment and automotive electronics. With the continuous development of electronic technology, the performance and quality requirements of PCB are gradually increasing. In particular, in the manufacturing process of complex circuit boards, the copper plating process (usually used to fill the conductive channels on the PCB) plays a vital role. The copper plating process aims to fill the conductive areas of the PCB surface with copper plating and keep the non-conductive areas clean to ensure the reliability and conductive performance of the circuit. Therefore, real-time monitoring and evaluation of PCB surface quality has become an indispensable part of the modern PCB manufacturing process.

[0003] Although there are many methods for detecting the surface quality of PCBs, such as X-ray and microscope detection, most of these methods have some obvious shortcomings. First, in the copper plating process, due to the differences in the distribution of the plating solution, the immersion time and the current density, local over-plating or under-plating is prone to occur. These small quality differences are often difficult to be discovered and corrected in time by traditional manual detection methods. Secondly, on the surface of the PCB, the small differences between the conductive and non-conductive areas in different areas are often difficult to be captured by existing detection technologies. These deficiencies will cause unqualified plating areas to be missed, thereby affecting the conductivity and reliability of the entire circuit board. If the quality defects in copper plating cannot be discovered in time, it will directly affect the quality of the final product and even cause the product to fail in subsequent use. In addition, misjudging the non-conductive area as a conductive area or misidentifying the conductive area that is insufficiently plated will lead to inaccurate quality sorting in the production process, affecting the efficiency of the entire production line, and in severe cases, it may even cause the outflow of unqualified products, affecting the company's reputation and market competitiveness. Therefore, a more accurate and automated PCB plating quality monitoring method is urgently needed. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a PCB copper electroplating quality monitoring method and system based on video image analysis, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A PCB copper plating quality monitoring method based on video image analysis comprises the following steps: S1. Use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and extract continuous image frames after image preprocessing the video stream, and obtain a set of PCB surface image frames before and after electroplating by splicing; S2. According to the PCB design drawing, identify the non-conductive area and the conductive area on the PCB surface, and combine the PCB surface image frame set before and after electroplating to obtain the grayscale difference of each pixel of the PCB before and after electroplating. Based on the grayscale difference of each pixel before and after electroplating of PCB , set the PCB surface area division threshold W; S3, according to the PCB surface area division threshold W set in S2, identifying whether there is a pseudo conductive area on the surface of the PCB after electroplating, if not, monitoring the relevant gloss data information of the PCB surface, and analyzing the electroplating state in the conductive area of ​​the PCB surface according to the relevant gloss data information, so as to construct an electroplating quality evaluation index Dpzs; S4. Preset an evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

[0006] Preferably, the specific steps of S1 include: S11, using a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles, and during the shooting process, using an LED array to evenly illuminate the PCB surface to obtain a video stream; S12. Use median filtering to denoise the video stream, and use the open source computer vision library OpenCV to read each frame of the video stream to form continuous image frames. Use a scale-invariant feature transformation algorithm to find the same feature points in two adjacent groups of image frames in the continuous image frames, and use a homography matrix to perform perspective transformation on the continuous image frames, align the image of the next frame to the image of the current frame to form a set of PCB surface image frames before and after electroplating, wherein the set of PCB surface image frames before and after electroplating includes a set of PCB surface image frames before electroplating and a set of PCB surface image frames after electroplating.

[0007] Preferably, the specific steps of S2 include: S21, first converting each frame image in the PCB surface image frame set before and after electroplating into a grayscale image type; S22, obtaining the PCB design drawing from the PCB manufacturer in advance, and identifying the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and marking them, and calculating the grayscale difference of each pixel of the PCB before and after the electroplating by combining the PCB surface image frame set before and after the electroplating , wherein the grayscale value before electroplating at each pixel on the PCB surface is obtained by extracting features from the PCB surface image frame set before and after electroplating. And the gray value after electroplating at each pixel on the PCB surface ; The grayscale difference of each pixel of the PCB before and after electroplating Obtained by the following formula: .

[0008] Preferably, the specific step S2 also includes: S23, based on the grayscale image type converted from each frame image, record the number of pixels of each grayscale level in the grayscale image to calculate the grayscale histogram H in the PCB surface image frame set before and after electroplating, wherein the grayscale value range in the grayscale image type is: , the grayscale histogram H is expressed as: in, are the number of pixels with gray level 0, the number of pixels with gray level 1, ..., the number of pixels with gray level 255; S24. Based on the content of S23, analyze the differences between the non-conductive area and the conductive area of ​​the PCB under different gray levels after electroplating to obtain the regional difference factors under different gray levels. , which is obtained according to the following formula: ; In the formula, It is expressed as the regional difference factor when the gray level is T; It is expressed as the ratio of the number of pixels before the gray level is T to the total number of pixels; It is expressed as the ratio of the number of pixels after the gray level is T to the total number of pixels; It is expressed as the average gray value of the pixels before the gray level is T; Expressed as the average gray value of pixels after the gray level is T.

[0009] Preferably, the specific step S2 also includes: S25, the regional difference factors under different gray levels obtained in S24 Statistics are performed to generate condition groups and the PCB surface area division threshold W is set in the following way: S251. Extract the regional difference factors according to the condition group The maximum gray level condition is recorded as the final gray level , the final gray level This is expressed in the following way: in, Represents the regional difference factor Maximum gray level condition; S252, the final gray level As the PCB surface area division threshold W.

[0010] Preferably, the specific steps of S3 include: S31, based on the PCB surface area division threshold W set in S25, and combined with the grayscale difference of each pixel of the PCB before and after electroplating , in order to identify whether the non-conductive area on the surface of the electroplated PCB is incorrectly electroplated. The specific identification content is as follows: S311, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that copper electroplating has been performed at the corresponding pixel on the PCB surface; S312, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that the copper plating operation has not been performed at the corresponding pixel on the PCB surface; S313, according to the contents of S311 and S312, and in combination with the non-conductive area and the conductive area of ​​the CB surface identified in S22, the contents of S311 and S312 are judged again. If the corresponding pixel in S311 has been subjected to copper plating and belongs to the conductive area of ​​the CB surface identified in S22, it is judged that the conductive area of ​​the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating; if the corresponding pixel in S311 has been subjected to copper plating and belongs to the non-conductive area of ​​the CB surface identified in S22, it is judged that the conductive area of ​​the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating. The copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel of the PCB surface after plating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the conductive area of ​​the CB surface identified in S22, it is judged that the copper plating operation is not performed on the conductive area at the corresponding pixel of the PCB surface after plating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the non-conductive area of ​​the CB surface identified in S22, it is judged that the copper plating operation is not performed on the non-conductive area at the corresponding pixel of the PCB surface after plating; S314, if it is determined that the copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel on the surface of the electroplated PCB, the corresponding pixel is regarded as a pseudo-conductive area, and a first quality monitoring end instruction is triggered externally; S315. If it is determined that the conductive area at the corresponding pixel on the surface of the PCB after electroplating has not been subjected to the copper plating operation, a No. 1 quality monitoring end instruction will be triggered externally.

[0011] Preferably, the specific step S3 also includes: S32: If it is determined that the conductive area of ​​the PCB surface after electroplating has been subjected to copper plating, and it is determined that the non-conductive area of ​​the PCB surface after electroplating has not been subjected to copper plating, then the gloss data information of the PCB surface after electroplating will continue to be monitored, wherein the gloss data information includes the intensity of the specular reflection light. , diffuse light intensity , incident light intensity and transmitted light intensity ; S33, based on the relevant gloss data information, respectively obtain the reflection coefficient Fsxs and the optical density Gxmd in the conductive area of ​​the PCB surface, specifically in the following manner: ; In the formula, Expressed as the specular light intensity, Expressed as diffuse light intensity; ; In the formula, Expressed as the incident light intensity, Expressed as transmitted light intensity; S34, based on the grayscale difference of each pixel before and after electroplating , analyze the differences between pixels in the conductive area of ​​the PCB surface to calculate the difference coefficient Cyxs, which can be obtained in the following way: , In the formula, Expressed as the standard deviation of the grayscale difference within the conductive area, Expressed as the average grayscale difference within the conductive area.

[0012] Preferably, the specific step S3 also includes: S35, by correlating the reflection coefficient Fsxs, the optical density Gxmd and the difference coefficient Cyxs, and after linear normalization, analyzing the electroplating state in the conductive area of ​​the PCB surface to construct an electroplating quality evaluation index Dpzs, the electroplating quality evaluation index Dpzs is obtained by the following formula: ; In the formula, and are the weight values ​​of optical density Gxmd, difference coefficient Cyxs and reflection coefficient Fsxs, is the correction constant, where and The specific value is set by the user according to the situation.

[0013] Preferably, the specific steps of S4 include: S41, by comparing and analyzing the electroplating quality evaluation index Dpzs with the evaluation threshold K, the quality status of the current PCB surface after copper electroplating is comprehensively judged, and the specific contents are as follows: If the electroplating quality evaluation index Dpzs exceeds the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is unqualified, and the first quality monitoring end instruction will be triggered; If the electroplating quality evaluation index Dpzs does not exceed the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is in a qualified state, and the second quality monitoring end instruction will be triggered; S42: Based on the corresponding instructions triggered in S41 and S31, corresponding sorting means are adopted, as follows: If the No. 1 quality monitoring end instruction is triggered, the corresponding PCB will be transported to the sorting area for manual intervention; If the second quality monitoring end instruction is triggered, the corresponding PCB will be transported to the next monitoring process.

[0014] PCB copper electroplating quality monitoring system based on video image analysis, including image capture module, pseudo-conductive area recognition module, electroplating quality inspection module and sorting feedback module; The image capture module is used to use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and after image preprocessing the video stream, extract continuous image frames, and obtain a set of PCB surface image frames before and after electroplating by splicing; The pseudo-conductive area recognition module is used to identify the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and to obtain the grayscale difference of each pixel of the PCB before and after the electroplating by combining the PCB surface image frame set before and after the electroplating. , based on the grayscale difference of each pixel before and after electroplating , set the PCB surface area division threshold W; The electroplating quality inspection module is used to identify whether there is a pseudo-conductive area on the surface of the electroplated PCB according to the set PCB surface area division threshold W. If not, the relevant gloss data information of the PCB surface is monitored, and the electroplating state in the conductive area of ​​the PCB surface is analyzed according to the relevant gloss data information to construct the electroplating quality evaluation index Dpzs; The sorting feedback module is used to pre-set the evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

[0015] The present invention provides a PCB copper electroplating quality monitoring method and system based on video image analysis, which has the following beneficial effects: (1) By using a CMOS camera to shoot the PCB surface from multiple angles and combining image stitching technology to obtain a set of continuous image frames before and after electroplating, the PCB surface can be fully covered and the slight quality differences that may occur during the copper electroplating process can be accurately captured. By analyzing the grayscale difference of each pixel, the detection accuracy of the electroplating quality is effectively improved, local electroplating problems are discovered, and the traditional detection methods are further avoided from missing or misjudgment of local areas. Combined with the PCB design drawing, the conductive and non-conductive areas before and after electroplating are automatically identified, and the surface area division threshold is set by grayscale difference analysis. It can automatically identify whether there is a pseudo-conductive area, thereby realizing fully automated quality inspection, reducing manual intervention and misjudgment. This method effectively reduces the error of manual inspection and improves the reliability and consistency of the production process. This method can not only obtain the glossiness and electroplating status information in real time during the electroplating process, but also make a comprehensive judgment based on the electroplating quality evaluation index Dpzs to timely discover quality problems in the electroplating process. By comparing and analyzing the set evaluation threshold K with the electroplating quality evaluation index Dpzs, the electroplating quality of the PCB surface can be quickly evaluated, and the decision of automatic sorting or manual intervention can be triggered based on the evaluation results to ensure that unqualified products do not flow into the subsequent production links. In short, this method can comprehensively and accurately detect the quality problems of PCB electroplating, reduce the risk of defective products flowing out due to misjudgment or missed judgment in traditional detection methods, and improve the overall efficiency and quality control capabilities of the production line. By reducing manual intervention and improving detection accuracy, production costs can be reduced, and the production cycle can be shortened while improving product quality. The monitoring of PCB surface quality is achieved through video image analysis, avoiding any physical damage or unnecessary contact to the circuit board, ensuring the integrity of the material and accurate detection of the electroplated surface. Compared with traditional contact detection methods, this non-destructive detection method can better meet the quality control needs of modern precision electronic products.

[0016] (2) Through the calculation and analysis of regional difference factors, the system can more accurately control the quality of the copper layer during the electroplating process, accurately divide the conductive and non-conductive areas, and ensure that only the areas that need to be electroplated obtain the copper layer. Through the analysis of grayscale differences and regional difference factors, it can accurately determine whether each area is correctly electroplated, and ensure that there are no errors in the electroplating quality between the conductive area and the non-conductive area, thereby reducing the problem of mis-plating or missed plating during the electroplating process, ensuring the consistency and quality of the product, and reducing rework and cost waste caused by quality problems; (3) After the system determines that there is no pseudo-conductive area on the PCB surface, it monitors the gloss data closely related to the electroplating quality for the electroplating area, thereby obtaining more detailed electroplating surface quality information. These gloss data provide an objective reflection of the changes in the surface optical properties and are important parameters for evaluating the electroplating quality. Through step S34, the system calculates the difference coefficient based on the standard deviation of the grayscale difference and the average grayscale difference of the conductive area. This coefficient can accurately reflect the grayscale changes of different pixels in the conductive area. A large difference coefficient may mean that the electroplating quality is uneven or defective. By calculating the reflection coefficient and optical density, combined with the grayscale difference coefficient, a more comprehensive quality assessment of the electroplating surface can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a method for monitoring PCB copper electroplating quality based on video image analysis according to the present invention; Figure 2 The present invention is a block diagram of a PCB copper electroplating quality monitoring system based on video image analysis. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1 See also Figure 1 The present invention provides a PCB copper electroplating quality monitoring method based on video image analysis, comprising the following steps: S1. Use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and extract continuous image frames after image preprocessing the video stream, and obtain a set of PCB surface image frames before and after electroplating by splicing; S2. According to the PCB design drawing, identify the non-conductive area and the conductive area on the PCB surface, and combine the PCB surface image frame set before and after electroplating to obtain the grayscale difference of each pixel of the PCB before and after electroplating. , based on the grayscale difference of each pixel before and after electroplating , set the PCB surface area division threshold W; S3, according to the PCB surface area division threshold W set in S2, identifying whether there is a pseudo conductive area on the surface of the PCB after electroplating, if not, monitoring the relevant gloss data information of the PCB surface, and analyzing the electroplating state in the conductive area of ​​the PCB surface according to the relevant gloss data information, so as to construct an electroplating quality evaluation index Dpzs; S4. Preset an evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

[0020] In this embodiment, by shooting from multiple perspectives and using image stitching technology, the method can obtain PCB surface images before and after electroplating in many aspects, ensuring that the quality monitoring of each area can be covered, and further avoiding the blind spot problem caused by the viewing angle limitation of the traditional detection method. The grayscale difference is accurately extracted using image processing technology, and the quality defects that may exist in the electroplating process can be efficiently and accurately analyzed, especially for tiny uneven electroplating or local electroplating defects. The method automatically identifies the non-conductive area and the conductive area on the PCB surface by combining the PCB design drawing, and on this basis, sets the PCB surface area division threshold W, so that the pseudo-conductive area after electroplating can be effectively identified, avoiding the error of manual intervention. By automatically judging the electroplating state and the electroplating accuracy of the non-conductive area, the bad electroplating area can be found in time to ensure the efficient implementation of the electroplating process.

[0021] Example 2 Please refer to Figure 1 , specifically: S1 specific steps include: S11, using a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles, and during the shooting process, using an LED array to evenly illuminate the PCB surface to obtain a video stream; S12. Use the median filtering method to denoise the video stream, and use the open source computer vision library OpenCV to read each frame of the video stream to form continuous image frames. Use the scale-invariant feature transform algorithm (SIFT) to find the same feature points in two adjacent groups of image frames in the continuous image frames, and use the homography matrix (HomographyMatrix) to perform perspective transformation on the continuous image frames, align the image of the next frame to the image of the current frame (for example, splice the transformed second image with the first image) to form a set of PCB surface image frames before and after electroplating, wherein the set of PCB surface image frames before and after electroplating includes a set of PCB surface image frames before electroplating and a set of PCB surface image frames after electroplating.

[0022] The specific steps of S2 include: S21, first converting each frame image in the PCB surface image frame set before and after electroplating into a grayscale image type; S22, obtaining the PCB design drawing from the PCB manufacturer in advance, and identifying the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and marking them, and calculating the grayscale difference of each pixel of the PCB before and after the electroplating by combining the PCB surface image frame set before and after the electroplating , wherein the grayscale value before electroplating at each pixel (position) on the PCB surface is obtained by extracting features from the PCB surface image frame set before and after electroplating. And the gray value after electroplating at each pixel on the PCB surface ; The grayscale difference of each pixel of the PCB before and after electroplating Obtained by the following formula: .

[0023] In this embodiment, by using a CMOS camera and an LED array to evenly illuminate the PCB surface and shoot at different viewing angles, the PCB surface image data before and after electroplating can be fully collected. Due to the use of multi-view shooting, the changes in surface features at different angles can be better captured, and the defect that only a single viewing angle cannot fully reflect the details of the PCB surface can be further avoided. Uniform illumination helps to eliminate the impact of uneven illumination on image quality, ensure that the image brightness and contrast of all areas are consistent, and increase the reliability of subsequent analysis. Using the median filter method to denoise the video stream can effectively remove noise and interference information in the image, and ensure that subsequent image processing (such as feature extraction and registration) is not interfered by noise. This is a key step in image processing, which helps to improve the accuracy of image analysis and ensure that only effective surface information is collected and processed. The scale-invariant feature transform (SIFT) algorithm and the homography matrix (Homography Matrix) are used to match feature points and transform perspectives on continuous image frames, so as to ensure that the image frames before and after electroplating are correctly aligned in space. This step ensures that images from different viewing angles can be seamlessly spliced ​​to generate a unified set of PCB surface images before and after electroplating, which is very important for subsequent comparison and analysis of the electroplating process at different time points. The information obtained from the PCB design drawing and combined with the image frame set before and after electroplating can accurately mark the conductive area and non-conductive area. This marking provides an important reference framework for subsequent analysis, helping to distinguish the electroplating status of different types of areas and reduce errors and interference. By calculating the grayscale difference before and after electroplating, the change in electroplating quality can be quantitatively analyzed, indicating the grayscale difference before and after electroplating of each pixel, and then evaluating the electroplating thickness, uniformity and whether there are electroplating defects (such as local non-electroplating or over-electroplating) in each area. This quantitative grayscale difference analysis can not only reveal the overall electroplating quality, but also be specific to each pixel level, accurately pointing out the potential problems in the electroplating process. Combined with the grayscale difference data before and after electroplating, the uniformity of the electroplating process and the stability of the electroplating quality can be further evaluated. If the grayscale difference in some areas is found to be too large, it may mean that the electroplating layer is too thick or too thin, which in turn affects the electrical or mechanical properties of the PCB. In this way, not only can electroplating defects be discovered in a timely manner, but also the electroplating process can be optimized based on the statistical results of the grayscale difference, the variation in the production process can be reduced, and the overall product quality can be improved. The use of image processing technology and automated grayscale difference calculation can achieve automated detection and real-time monitoring, which means that the electroplating quality of each PCB can be evaluated instantly during the production process without manual intervention, saving labor costs and improving production efficiency. In addition, the system can automatically generate a test report to feedback the electroplating quality of each PCB to ensure that each product meets the predetermined quality standards.

[0024] Example 3 Please refer to Figure 1 Specifically: S2 includes the following specific steps: S23, based on the grayscale image type converted from each frame image, record the number of pixels of each grayscale level in the grayscale image to calculate the grayscale histogram H in the PCB surface image frame set before and after electroplating, wherein the grayscale value range in the grayscale image type is: , the grayscale histogram H is expressed as: in, are the number of pixels with gray level 0, the number of pixels with gray level 1, ..., the number of pixels with gray level 255; S24. Based on the content of S23, analyze the differences between the non-conductive area and the conductive area of ​​the PCB under different gray levels after electroplating to obtain the regional difference factors under different gray levels. , which is obtained according to the following formula: ; In the formula, It is expressed as the regional difference factor when the gray level is T; It is expressed as the ratio of the number of pixels before the gray level is T to the total number of pixels; It is expressed as the ratio of the number of pixels after the gray level is T to the total number of pixels; It is expressed as the average gray value of the pixels before the gray level is T; It is expressed as the average gray value of pixels after the gray level is T; It represents the difference in mean between non-conductive area and conductive area. If the difference in mean between two areas is large, then the regional difference factor will be large even if the difference in number of pixels between the two areas is large. S241, the ratio of the number of pixels before the gray level is T to the total number of pixels Obtained by the following formula: ; In the formula, Represented as the height of the grayscale image, Represented as the length of the grayscale image, is the total number of pixels in the grayscale image, It means when the gray level is The number of pixels when is the corresponding gray level condition, ; S242, at gray level The ratio of the number of pixels after to the total number of pixels Obtained by the following formula: ; In the formula, ; S243, at gray level The average gray value of the previous pixel Obtained by the following formula: ; S245, at gray level The average gray value of the pixels after Obtained by the following formula: ; In the formula, Indicates grayscale The total contribution to brightness present in the region.

[0025] The specific steps of S2 also include: S25, the regional difference factors under different gray levels obtained in S24 Statistics are performed to generate condition groups and the PCB surface area division threshold W is set in the following way: S251. Extract the regional difference factors according to the condition group The maximum gray level condition is recorded as the final gray level , the final gray level This is expressed in the following way: in, Represents the regional difference factor Maximum gray level condition; S252, the final gray level As the PCB surface area division threshold W.

[0026] In this embodiment, calculation of the grayscale histogram: by recording the number of pixels at each grayscale level, the system can perform a comprehensive grayscale distribution analysis on the set of PCB surface image frames before and after electroplating. The grayscale histogram H provides the grayscale distribution information of the surface images before and after electroplating, providing basic data for subsequent analysis of regional differences. Grayscale value range: In a grayscale image, the grayscale value range is usually 0 to 255, representing pixel values ​​from the darkest (black) to the brightest (white). Through the analysis of the grayscale histogram, the system can identify the grayscale differences between different areas (such as conductive areas and non-conductive areas), and then evaluate the quality of electroplating. By calculating the difference between the conductive area and the non-conductive area under different grayscale conditions, the regional difference factor can be obtained. , this factor helps to quantify the grayscale difference between the two areas, thereby effectively reflecting the unplated and plated areas that may appear during the electroplating process. A larger regional difference factor indicates that the quality of the electroplated layer in these areas during the electroplating process is relatively different, which is conducive to dividing the electroplated and non-plated areas on the PCB surface; by calculating the proportion of the number of pixels before and after the grayscale level T to the total number of pixels, it helps to quantify the change in pixel distribution, which can reflect the change in the number of pixels in the conductive and non-conductive areas between different grayscale levels, especially before and after electroplating. By calculating the average grayscale value of the pixels before and after the grayscale level T, the grayscale change trend in different areas can be obtained. If the mean difference between the two areas is large, the regional difference factor will increase. According to the above statistical results, the grayscale condition with the largest regional difference factor is selected as the final grayscale, marking the critical point of the change of electroplating quality at this grayscale. This threshold helps to accurately separate the non-conductive areas that do not need to be plated from the conductive areas that need to be plated during the electroplating process, thereby improving the accuracy of electroplating quality control. After determining the threshold W, the conductive area and the non-conductive area can be accurately divided, thereby ensuring that the copper layer is electroplated in the conductive area and avoiding copper plating in the non-conductive area. This provides strong data support for quality control in the electroplating process and helps to discover possible excess plating or leakage in the electroplating process.

[0027] Example 4 Please refer to Figure 1 , specifically: S3 specific steps include: S31, based on the PCB surface area division threshold W set in S25, and combined with the grayscale difference of each pixel of the PCB before and after electroplating , in order to identify whether the non-conductive area on the surface of the electroplated PCB is incorrectly electroplated. The specific identification content is as follows: S311, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that copper electroplating has been performed at the corresponding pixel on the PCB surface; S312, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that the copper electroplating operation has not been performed at the corresponding pixel on the PCB surface; S313, according to the contents of S311 and S312, and in combination with the non-conductive area and the conductive area of ​​the CB surface identified in S22, the contents of S311 and S312 are judged again. If the corresponding pixel in S311 has been subjected to copper plating and belongs to the conductive area of ​​the CB surface identified in S22, it is judged that the conductive area of ​​the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating; if the corresponding pixel in S311 has been subjected to copper plating and belongs to the non-conductive area of ​​the CB surface identified in S22, it is judged that the conductive area of ​​the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating. The copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel of the PCB surface after plating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the conductive area of ​​the CB surface identified in S22, it is judged that the copper plating operation is not performed on the conductive area at the corresponding pixel of the PCB surface after plating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the non-conductive area of ​​the CB surface identified in S22, it is judged that the copper plating operation is not performed on the non-conductive area at the corresponding pixel of the PCB surface after plating; S314, if it is determined that the copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel on the surface of the electroplated PCB, the corresponding pixel is regarded as a pseudo-conductive area, and a first quality monitoring end instruction is triggered externally; S315. If it is determined that the conductive area at the corresponding pixel on the surface of the PCB after electroplating has not been subjected to the copper plating operation, a No. 1 quality monitoring end instruction will be triggered externally.

[0028] In this embodiment, by combining and analyzing the grayscale difference at each pixel before and after electroplating with the PCB surface area division threshold W, it is possible to accurately identify whether the conductive area and non-conductive area after electroplating are correctly electroplated. This method helps to detect incorrect electroplating, that is, the non-conductive area is mistakenly electroplated as a conductive area or the conductive area is omitted from electroplating, thereby ensuring the accuracy and quality of the electroplating process. S311 and S312: By comparing whether the grayscale difference before and after electroplating exceeds the set threshold W, the system can determine whether each pixel has been subjected to copper plating. This mechanism can timely discover quality problems during the production process and provide timely feedback to reduce waste and unqualified products in production; when the system finds that the conductive area has not been electroplated, it will also trigger the No. 1 quality monitoring end instruction, indicating that there are defects in the production process, avoiding the occurrence of missed plating areas, and ensuring the quality and performance of the PCB. And through automated grayscale difference calculation and area division analysis, the system can reduce the burden of manual inspection, thereby reducing labor costs. Manual intervention only needs to be carried out when serious problems occur, and the system can complete quality judgment by automated monitoring at other times.

[0029] Example 5 Please refer to Figure 1, specifically: S3 specific steps also include: S32: If it is determined that the conductive area of ​​the PCB surface after electroplating has been subjected to copper plating, and it is determined that the non-conductive area of ​​the PCB surface after electroplating has not been subjected to copper plating, then the gloss data information of the PCB surface after electroplating will continue to be monitored, wherein the gloss data information includes the intensity of the specular reflection light. , diffuse light intensity , incident light intensity and transmitted light intensity ; S33, based on the relevant gloss data information, respectively obtain the reflection coefficient Fsxs and the optical density Gxmd in the conductive area of ​​the PCB surface, specifically in the following manner: ; In the formula, Expressed as the specular light intensity, Expressed as diffuse light intensity; ; In the formula, Expressed as the incident light intensity, Expressed as transmitted light intensity; S34, based on the grayscale difference of each pixel before and after electroplating , analyze the differences between pixels in the conductive area of ​​the PCB surface to calculate the difference coefficient Cyxs, which can be obtained in the following way: , In the formula, Expressed as the standard deviation of the grayscale difference within the conductive area, Expressed as the average grayscale difference within the conductive area.

[0030] The above specular reflection light intensity It refers to the intensity of light reflected from a smooth surface according to the law of reflection, usually measured using a spectroradiometer or a reflectance photometer; Diffuse light intensity Refers to the intensity of light that is randomly reflected when light hits an irregular surface. Diffuse reflection photometers are usually used to measure diffuse reflection light intensity. Such instruments scatter the light from the light source over the entire surface and measure the intensity of the light reflected back at different angles. Incident light intensity It refers to the intensity of light irradiated onto the target surface, usually measured by a light meter or radiometer, which can measure the light intensity per unit area at a certain location; Transmitted light intensity It refers to the intensity of light transmitted after passing through a translucent material. Transmitted light intensity is usually measured using a transmission photometer or a spectral transmittance meter. These instruments can accurately measure the intensity of transmitted light through a PCB or other materials.

[0031] In this embodiment, gloss monitoring: by monitoring the incident light intensity, transmitted light intensity, specular reflection light intensity and diffuse reflection light intensity and other related gloss data information, the gloss of the PCB surface after electroplating can be deeply analyzed. This data support can effectively judge the optical characteristics in the electroplating process, especially the gloss difference between the conductive area and the non-conductive area, so as to further evaluate the electroplating quality; by calculating based on the gloss data information, the reflection coefficient and optical density are obtained, and the changes in the surface reflection and transmission light of the conductive area can be accurately evaluated, and then the uniformity and quality of the surface electroplating can be judged. Through the in-depth analysis of the optical characteristic data and grayscale differences, the continuous improvement of the electroplating quality can be promoted. After each electroplating, the system can provide a detailed optical analysis report, providing improvement directions and specific operation suggestions for subsequent production. This series of steps can accurately judge the changes in optical characteristics in the electroplating process through gloss data information and grayscale difference analysis, and further evaluate the electroplating uniformity of the conductive area.

[0032] Example 6 Please refer to Figure 1 , specifically: S3 specific steps also include: S35, by correlating the reflection coefficient Fsxs, the optical density Gxmd and the difference coefficient Cyxs, and after linear normalization, analyzing the electroplating state in the conductive area of ​​the PCB surface to construct an electroplating quality evaluation index Dpzs, the electroplating quality evaluation index Dpzs is obtained by the following formula: ; In the formula, and are the weight values ​​of optical density Gxmd, difference coefficient Cyxs and reflection coefficient Fsxs, is the correction constant, where and The specific value is set by the user according to the situation.

[0033] Among them, the larger the optical density Gxmd, the stronger the optical absorption characteristics of the deposited layer on the PCB surface, which usually indicates that the surface is smoother and more uniform, the electroplating layer is thicker, and the light scattering is smaller. This may be due to the uniform and dense copper electroplating layer, which leads to increased light absorption. Higher optical density is usually associated with better quality electroplating layer, indicating that the copper layer on the PCB surface is denser and has no obvious defects. Optical density is a parameter to measure the material's ability to absorb light, which usually refers to the attenuation of light intensity after light passes through the material. In the evaluation of PCB electroplating quality, optical density can reflect the uniformity and optical characteristics of the electroplating layer. The reflection coefficient Fsxs is a parameter that describes the ability of a surface to reflect light, and reflects the smoothness of the surface and the uniformity of the copper layer. The larger the reflection coefficient, the stronger the intensity of the reflected light, which usually indicates that the PCB surface is smooth, the copper plating layer is uniform and flat, and the smooth surface can reflect more incident light, which also implies that the uniformity and thickness of the plating layer are appropriate, and it is suitable for high-precision electronic product manufacturing.

[0034] The specific steps of S4 include: S41, by comparing and analyzing the electroplating quality evaluation index Dpzs with the evaluation threshold K, the quality status of the current PCB surface after copper electroplating is comprehensively judged, and the specific contents are as follows: If the electroplating quality evaluation index Dpzs exceeds the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is unqualified, and the first quality monitoring end instruction will be triggered; If the electroplating quality evaluation index Dpzs does not exceed the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is in a qualified state, and the second quality monitoring end instruction will be triggered; S42: Based on the corresponding instructions triggered in S41 and S31, corresponding sorting means are adopted, as follows: If the No. 1 quality monitoring end instruction is triggered, the corresponding PCB will be transported to the sorting area for manual intervention; If the second quality monitoring end instruction is triggered, the corresponding PCB will be transported to the next monitoring process.

[0035] In this embodiment, the electroplating quality evaluation index Dpzs is constructed by combining the three key indicators of reflection coefficient, optical density and coefficient of difference with linear normalization processing. This evaluation index integrates different physical property data and provides a more comprehensive electroplating quality evaluation model, which can accurately analyze different areas (conductive areas and non-conductive areas) on the PCB surface. Flexibility of weights and correction constants: By setting weight values ​​and correction constants, the evaluation model has strong flexibility and can be adjusted according to different production conditions and user needs, thereby ensuring that the electroplating quality can be effectively evaluated under various circumstances. Optical density: reflects the thickness and optical properties of the deposited layer on the surface, affecting the uniformity of electroplating. Coefficient of difference: reflects the uniformity of the electroplating process. A larger value may indicate uneven electroplating or defects. Reflection coefficient: evaluates the surface smoothness and optical properties of the electroplating layer. A high reflection coefficient usually means a smooth and uniform electroplating layer. Electroplating quality evaluation index comparison threshold: By comparing with the preset evaluation threshold, the system can automatically determine whether the current electroplating quality is qualified. If the electroplating quality evaluation index exceeds the threshold, it means that the electroplating quality does not meet the standard, and the system will automatically trigger the No. 1 quality monitoring end instruction; if it does not exceed the threshold, it is judged to be qualified and triggers the No. 2 quality monitoring end instruction. This automated quality judgment process reduces the error and delay of manual judgment, improves the intelligence level of the production line, and makes the production process more efficient and reliable. Automated sorting system: By triggering the No. 1 and No. 2 quality monitoring end instructions, the system can respond quickly and automatically transport unqualified PCBs to the manual intervention sorting area, and qualified PCBs continue to enter the next process. This not only improves the sorting efficiency, but also ensures that unqualified products will not continue to enter the production process, reducing quality problems from the source. Combination of manual intervention and automation: By sending unqualified PCBs to the manual intervention sorting area, a more flexible quality intervention method is provided for the production line to ensure the timely resolution of quality problems. Reduce the production of substandard products: The automated quality assessment and sorting system further reduces human errors and quality omissions, ensuring that the electroplating quality of each PCB is accurately assessed.

[0036] Example 7 Please refer to Figure 2 ,Specifically: PCB copper electroplating quality monitoring system based on video image analysis, including image capture module, pseudo conductive area recognition module, electroplating quality inspection module and sorting feedback module; The image capture module is used to use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and after image preprocessing the video stream, extract continuous image frames, and obtain a set of PCB surface image frames before and after electroplating by splicing; The pseudo-conductive area recognition module is used to identify the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and to obtain the grayscale difference of each pixel of the PCB before and after the electroplating by combining the PCB surface image frame set before and after the electroplating. , based on the grayscale difference of each pixel before and after electroplating , set the PCB surface area division threshold W; The electroplating quality inspection module is used to identify whether there is a pseudo-conductive area on the surface of the electroplated PCB according to the set PCB surface area division threshold W. If not, the relevant gloss data information of the PCB surface is monitored, and the electroplating state in the conductive area of ​​the PCB surface is analyzed according to the relevant gloss data information to construct the electroplating quality evaluation index Dpzs; The sorting feedback module is used to pre-set the evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

[0037] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A PCB copper plating quality monitoring method based on video image analysis, characterized in that: The following steps are included: S1. Use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and extract continuous image frames after image preprocessing the video stream, and obtain a set of PCB surface image frames before and after electroplating by splicing; S2. According to the PCB design drawing, identify the non-conductive area and the conductive area on the PCB surface, and combine the PCB surface image frame set before and after electroplating to obtain the grayscale difference of each pixel of the PCB before and after electroplating. , based on the grayscale difference of each pixel before and after electroplating , set the PCB surface area division threshold W; S3, according to the PCB surface area division threshold W set in S2, identifying whether there is a pseudo conductive area on the surface of the PCB after electroplating, if not, monitoring the relevant gloss data information of the PCB surface, and analyzing the electroplating state in the conductive area of ​​the PCB surface according to the relevant gloss data information, so as to construct an electroplating quality evaluation index Dpzs; S4. Preset an evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

2. The PCB copper plating quality monitoring method based on video image analysis according to claim 1 is characterized in that: The specific steps of S1 include: S11, using a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles, and during the shooting process, using an LED array to evenly illuminate the PCB surface to obtain a video stream; S12. Use median filtering to denoise the video stream, and use the open source computer vision library OpenCV to read each frame of the video stream to form continuous image frames. Use a scale-invariant feature transformation algorithm to find the same feature points in two adjacent groups of image frames in the continuous image frames, and use a homography matrix to perform perspective transformation on the continuous image frames, align the image of the next frame to the image of the current frame to form a set of PCB surface image frames before and after electroplating, wherein the set of PCB surface image frames before and after electroplating includes a set of PCB surface image frames before electroplating and a set of PCB surface image frames after electroplating.

3. The PCB copper plating quality monitoring method based on video image analysis according to claim 2 is characterized in that: The specific steps of S2 include: S21, first converting each frame image in the PCB surface image frame set before and after electroplating into a grayscale image type; S22, obtaining the PCB design drawing from the PCB manufacturer in advance, and identifying the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and marking them, and calculating the grayscale difference of each pixel of the PCB before and after the electroplating by combining the PCB surface image frame set before and after the electroplating , wherein the grayscale value before electroplating at each pixel on the PCB surface is obtained by extracting features from the PCB surface image frame set before and after electroplating. And the gray value after electroplating at each pixel on the PCB surface ; The grayscale difference of each pixel of the PCB before and after electroplating Obtained by the following formula: .

4. The PCB copper plating quality monitoring method based on video image analysis according to claim 3 is characterized in that: The specific steps of S2 also include: S23, based on the grayscale image type converted from each frame image, record the number of pixels of each grayscale level in the grayscale image to calculate the grayscale histogram H in the PCB surface image frame set before and after electroplating, wherein the grayscale value range in the grayscale image type is: , the grayscale histogram H is expressed as: in, are the number of pixels with gray level 0, the number of pixels with gray level 1, ..., the number of pixels with gray level 255; S24. Based on the content of S23, analyze the differences between the non-conductive area and the conductive area of ​​the PCB under different gray levels after electroplating to obtain the regional difference factors under different gray levels. , which is obtained according to the following formula: ； In the formula, It is expressed as the regional difference factor when the gray level is T; It is expressed as the ratio of the number of pixels before the gray level is T to the total number of pixels; It is expressed as the ratio of the number of pixels after the gray level is T to the total number of pixels; It is expressed as the average gray value of the pixels before the gray level is T; Expressed as the average gray value of pixels after the gray level is T.

5. The PCB copper plating quality monitoring method based on video image analysis according to claim 4 is characterized in that: The specific steps of S2 also include: S25, the regional difference factors under different gray levels obtained in S24 Statistics are performed to generate condition groups and the PCB surface area division threshold W is set in the following way: S251. Extract the regional difference factors according to the condition group The maximum gray level condition is recorded as the final gray level , the final gray level This is expressed in the following way: in, Represents the regional difference factor Maximum gray level condition; S252, the final gray level As the PCB surface area division threshold W.

6. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 5 is characterized in that: The specific steps of S3 include: S31, based on the PCB surface area division threshold W set in S25, and combined with the grayscale difference of each pixel of the PCB before and after electroplating , in order to identify whether the non-conductive area on the surface of the electroplated PCB is incorrectly electroplated. The specific identification content is as follows: S311, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that copper electroplating has been performed at the corresponding pixel on the PCB surface; S312, if the grayscale difference of the corresponding pixel of the PCB before and after electroplating When the PCB surface area division threshold W is exceeded, it means that the copper electroplating operation has not been performed at the corresponding pixel on the PCB surface; S313, according to the contents of S311 and S312, and in combination with the non-conductive area and the conductive area of ​​the CB surface identified in S22, the contents of S311 and S312 are judged again. If the corresponding pixel in S311 has been subjected to copper plating and belongs to the conductive area of ​​the CB surface identified in S22, it is judged that the conductive area of ​​the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating; if the corresponding pixel in S311 has been subjected to copper plating and belongs to the non-conductive area of ​​the CB surface identified in S22, it is judged that the conductive area of ​​the corresponding pixel on the PCB surface after electroplating has been subjected to copper plating. The copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel of the PCB surface after plating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the conductive area of ​​the CB surface identified in S22, it is judged that the copper plating operation is not performed on the conductive area at the corresponding pixel of the PCB surface after plating; if the copper plating operation is not performed on the corresponding pixel in S312 and it belongs to the non-conductive area of ​​the CB surface identified in S22, it is judged that the copper plating operation is not performed on the non-conductive area at the corresponding pixel of the PCB surface after plating; S314, if it is determined that the copper plating operation is mistakenly performed on the non-conductive area at the corresponding pixel on the surface of the electroplated PCB, the corresponding pixel is regarded as a pseudo-conductive area, and a first quality monitoring end instruction is triggered externally; S315. If it is determined that the conductive area at the corresponding pixel on the surface of the PCB after electroplating has not been subjected to the copper plating operation, a No. 1 quality monitoring end instruction will be triggered externally.

7. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 6 is characterized in that: The specific steps of S3 also include: S32: If it is determined that the conductive area of ​​the PCB surface after electroplating has been subjected to copper plating, and it is determined that the non-conductive area of ​​the PCB surface after electroplating has not been subjected to copper plating, then the gloss data information of the PCB surface after electroplating will continue to be monitored, wherein the gloss data information includes the intensity of the specular reflection light. , diffuse light intensity , incident light intensity and transmitted light intensity ; S33, based on the relevant gloss data information, respectively obtain the reflection coefficient Fsxs and the optical density Gxmd in the conductive area of ​​the PCB surface, specifically in the following manner: ； In the formula, Expressed as the specular light intensity, Expressed as diffuse light intensity; ； In the formula, Expressed as the incident light intensity, Expressed as transmitted light intensity; S34, based on the grayscale difference of each pixel before and after electroplating , analyze the differences between pixels in the conductive area of ​​the PCB surface to calculate the difference coefficient Cyxs, which can be obtained in the following way: ， In the formula, Expressed as the standard deviation of the grayscale difference within the conductive area, Expressed as the average grayscale difference within the conductive area.

8. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 7 is characterized in that: The specific steps of S3 also include: S35, by correlating the reflection coefficient Fsxs, the optical density Gxmd and the difference coefficient Cyxs, and after linear normalization, analyzing the electroplating state in the conductive area of ​​the PCB surface to construct an electroplating quality evaluation index Dpzs, the electroplating quality evaluation index Dpzs is obtained by the following formula: ； In the formula, and are the weight values ​​of optical density Gxmd, difference coefficient Cyxs and reflection coefficient Fsxs, is the correction constant, where and The specific value is set by the user according to the situation.

9. The PCB copper electroplating quality monitoring method based on video image analysis according to claim 6 is characterized in that: The specific steps of S4 include: S41, by comparing and analyzing the electroplating quality evaluation index Dpzs with the evaluation threshold K, the quality status of the current PCB surface after copper electroplating is comprehensively judged, and the specific contents are as follows: If the electroplating quality evaluation index Dpzs exceeds the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is unqualified, and the first quality monitoring end instruction will be triggered; If the electroplating quality evaluation index Dpzs does not exceed the evaluation threshold K, it will be comprehensively judged that the quality state of the current PCB surface after copper electroplating is in a qualified state, and the second quality monitoring end instruction will be triggered; S42: Based on the corresponding instructions triggered in S41 and S31, corresponding sorting means are adopted, as follows: If the No. 1 quality monitoring end instruction is triggered, the corresponding PCB will be transported to the sorting area for manual intervention; If the second quality monitoring end instruction is triggered, the corresponding PCB will be transported to the next monitoring process.

10. A PCB copper electroplating quality monitoring system based on video image analysis, used to implement the PCB copper electroplating quality monitoring method based on video image analysis as described in any one of claims 1 to 9, characterized in that: It includes image capture module, pseudo-conductive area recognition module, electroplating quality inspection module and sorting feedback module; The image capture module is used to use a CMOS camera to shoot the PCB surface before and after electroplating from multiple angles to obtain a video stream, and after image preprocessing the video stream, extract continuous image frames, and obtain a set of PCB surface image frames before and after electroplating by splicing; The pseudo-conductive area recognition module is used to identify the non-conductive area and the conductive area on the PCB surface according to the PCB design drawing, and obtain the grayscale difference of each pixel of the PCB before and after electroplating by combining the PCB surface image frame set before and after electroplating. , based on the grayscale difference of each pixel before and after electroplating , set the PCB surface area division threshold W; The electroplating quality inspection module is used to identify whether there is a pseudo-conductive area on the surface of the electroplated PCB according to the set PCB surface area division threshold W. If not, the relevant gloss data information of the PCB surface is monitored, and the electroplating state in the conductive area of ​​the PCB surface is analyzed according to the relevant gloss data information to construct the electroplating quality evaluation index Dpzs; The sorting feedback module is used to pre-set the evaluation threshold K, and compare and analyze it with the electroplating quality evaluation index Dpzs to comprehensively judge the quality status of the current PCB surface after copper electroplating, and take corresponding sorting measures based on the judgment result.

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