CN114862817A - A method, system, device and medium for detecting defects in gold finger area of circuit board - Google Patents

Abstract

The invention discloses a method, system, device and medium for detecting defects in the gold finger area of a circuit board, and relates to the field of electronic component production. In the finger image area, the circuit image to be tested is divided according to the coordinates of the golden finger image area in the circuit image to be tested to obtain the image of the golden finger area, and then the image of the golden finger area is analyzed to obtain the printed circuit. The defect detection result of the circuit board can automatically detect the defects in the gold finger area of the printed circuit board through the computer, and avoid the unstable and inaccurate detection results obtained by manual visual inspection of the golden finger area with similar characteristics. , has strong practicability.

Description

A method, system, device and medium for detecting defects in gold finger area of circuit board

technical field

The invention relates to the field of electronic component production, in particular to a method, system, device and medium for detecting defects in the gold finger area of a circuit board.

Background technique

With the vigorous development of the electronic industry, the circuit design has become more and more complicated and detailed. As the main carrier of electronic product circuits, printed circuit boards have more and more stringent requirements for their manufacturing processes. There are multiple processes involved in the production process of printed circuit boards, and different processes may cause different degrees of defects on the surface of printed circuit boards. The gold finger area is an area on the printed circuit board composed of multiple golden-yellow conductive contacts for signal transmission. Because the surface is gold-plated and the conductive contacts are arranged like fingers, it is called "gold finger". In the printed circuit board, the gold finger, as the outlet of the external connection network, is a key area that affects the quality of the printed circuit board. At present, for the inspection of printed circuit boards, pictures are usually taken by an AOI automatic optical inspection machine, and then the defects are classified by manual visual inspection. However, manual visual inspection has great subjectivity, especially for gold with similar characteristics In the detection of the finger area, the long-term manual work will greatly affect the visual inspection results, resulting in inaccurate detection results.

SUMMARY OF THE INVENTION

In order to solve the problem of the detection of gold finger regions with similar characteristics in the existing detection of printed circuit boards, the long-term manual work will greatly affect the visual inspection results, resulting in unstable and inaccurate detection results. The present invention provides a method, system, device and medium for detecting defects in the golden finger area. By locating the golden finger area on the printed circuit board, and then analyzing the key area of the image obtained after the positioning, it can be automatically detected by a computer. Defects in the gold finger area of the printed circuit board.

In order to achieve the above purpose of the invention, the present invention provides a method for detecting defects in the gold finger region of a circuit board, comprising the following steps:

Obtain an image of the circuit to be tested;

Identifying the image of the circuit to be tested, obtaining the coordinates of the target area, and dividing the image of the circuit to be tested according to the coordinates of the target area to obtain an image of the golden finger area;

processing the image of the golden finger area to obtain image data;

The image data is analyzed to obtain defect detection results.

The principle of the present invention is: after obtaining the image of the circuit to be tested, the computer recognizes the image of the circuit to be tested, locates the image area of the golden finger in the circuit to be tested, and obtains the image of the golden finger in the image of the circuit to be tested. According to the coordinates in the test circuit, the circuit image to be tested is divided according to the coordinates to obtain an image of the golden finger area, and then a series of image processing algorithms are used to analyze the image of the golden finger area to obtain image data. After analysis, the defect detection results of the printed circuit board can be obtained.

Further, in order to accurately locate the golden finger area in the circuit, a target detection framework based on deep learning is used to obtain the coordinates of the target area, and the obtaining of the coordinates of the target area of the circuit to be tested includes the following steps:

Obtaining a plurality of circuit sample diagrams, marking the golden finger area in the circuit sample diagrams, and obtaining a first training set;

Train a first target detection framework based on the first training set to obtain a first target detection model;

The first target detection model is used to identify the image of the circuit to be tested, and the coordinates of the target detection area are obtained.

Further, if there is a residual copper defect in the gold finger area, it may cause a short circuit in the gold finger area during use, resulting in substandard quality of the printed circuit board. For the identification of the residual copper defect in the gold finger area, the target based on deep learning is adopted. The detection framework is implemented, therefore, the following steps are further included before the processing of the golden finger area image:

Obtaining a plurality of circuit defect sample maps, marking the defect positions and categories in the circuit defect sample maps, and obtaining a second training set;

Train a second target detection framework based on the second training set to obtain a second target detection model;

The processing of the golden finger area image to obtain image data includes the following steps:

The second target detection model is used to identify the golden finger region image to obtain an image identification result.

Among them, since it is necessary to manually mark the features in the sample set used to train the target detection framework to obtain the training set, in order to improve the recognition accuracy of the target detection model obtained after the training of the target detection framework, the required number of samples is as small as possible. There are many possibilities, which leads to a large workload. In order to reduce the workload, the Faster-RCNN target detection framework combined with the FPN structure is used, and the faster-RCNN target detection framework combined with the FPN structure is suitable for deep learning problems with small sample sizes. Effectively reduce the workload of preprocessing sample sets.

Further, if there is a contamination defect in the golden finger area, it may lead to poor circuit contact when the golden finger area is used, resulting in substandard quality of the printed circuit board. When identifying the contamination defect in the golden finger area, the The processing of the gold finger area image includes the following steps:

In order to facilitate computer image processing, binarization is performed on the golden finger region image to obtain a binarized image;

In order to filter out possible noise in the image and improve the accuracy of image processing, filtering the binarized image to obtain a first image;

The pixel gray value of the binarized image is 255 or 0. After the binarization process, the dirty pixels in the golden finger area will be distinguished from the pixels in the normal circuit image, and will be different from the bottom of the printed circuit board. The material area is displayed in black together, and the normal circuit part is displayed in white, so the contamination in the gold finger area can be realized by detecting the area of the connected area in the image, so the connected area in the first image is detected;

calculating the area of each connected region in the first image;

The analyzing the image data to obtain the defect detection result includes the following steps:

Since the golden finger area may be dirty, the presence of dirt will reduce the area of the normal part. Therefore, the size of the connected area is judged. If the area of the connected area is smaller than the standard value, then the golden finger area Image is defective.

Among them, in order to avoid missing detection due to the removal of the small contamination in the image of the golden finger region due to the filtering process, the filtering process is implemented by the method of morphological opening operation, first performing corrosion processing on the binarized image, and then performing corrosion processing on the binary image. Dilation processing is performed on the dilated image to obtain the first image, and the morphological opening operation can eliminate isolated white dots, burrs and small bridges outside the image, and keep the overall position and shape of the image unchanged.

Further, if there are metal oxidation defects in the gold finger area, the service life of the printed circuit board may be reduced, resulting in substandard quality of the printed circuit board. When identifying metal oxidation defects in the gold finger area, the The processing of the gold finger area image includes the following steps:

In order to facilitate computer image processing, grayscale processing is performed on the image of the golden finger region to obtain a grayscale image;

In order to filter out possible noise in the image and improve the accuracy of image processing, filtering the grayscale image to obtain a second image;

Since the detection of printed circuit boards is mostly carried out in an industrial production environment, the industrial light source used to obtain images is relatively stable. When the gold finger has metal oxidation defects, the color of the image in the gold finger area is darker than that of the standard gold finger area. However, the color of the substrate of the printed circuit board remains unchanged, so the average grayscale value of the image after oxidation will decrease. According to the grayscale average value of the image in the gold finger area, it can be determined whether there is an oxidation defect in the gold finger area of the printed circuit board. Therefore, calculating the grayscale average value of the second image to obtain second image data;

The analyzing the image data to obtain the defect detection result includes the following steps:

Comparing the size of the second image data with that of the standard image grayscale interval, if the second image data is not within the standard image grayscale interval, the golden finger area image is defective.

Among them, when there is a metal oxidation defect in the gold finger area, the original golden area becomes dark gold. Since the collected image is an RGB image, for an RGB image, gold and dark gold mainly exist in the R channel component and G channel component of the image. In order to avoid the influence of the B channel component on the gray average value of the image, the B channel component of the image can be compensated to keep the B channel component of the image fixed. Therefore, the circuit to be tested is photographed under blue light to obtain the Describe the image of the circuit under test.

Among them, since the collected images are RGB images, for RGB images, gold and dark gold are mainly different in the R channel component and G channel component of the image. In order to avoid the B channel component from affecting the average gray level of the image , when calculating the average gray level of the image, the B channel component of the image can be excluded, and the grayscale processing of the golden finger area image includes the following steps:

obtaining the R component and the G component of the image pixel of the golden finger area;

Weighted calculation is performed on the R component and the G component to obtain a pixel gray value;

A grayscale image is generated from the pixel grayscale values.

In order to achieve the above purpose of the invention, the present invention also provides a circuit board gold finger region defect detection system, the system includes:

an image obtaining unit, used to obtain an image of the circuit to be tested;

an area identification unit, configured to identify the image of the circuit to be tested, obtain coordinates of the target area, and segment the image of the circuit to be tested according to the coordinates of the target area to obtain an image of the golden finger area;

a processing unit, configured to process the image of the golden finger area to obtain image data;

an analysis unit for analyzing the image data to obtain a defect detection result;

The system is used for implementing the steps of the method for detecting defects in the gold finger region of the circuit board.

In order to achieve the above purpose of the invention, the present invention also provides a circuit board gold finger region defect detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, the processing When the computer executes the computer program, the computer implements the steps of the method for detecting defects in the gold finger region of the circuit board.

In order to achieve the above object of the invention, the present invention also provides a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the defect detection of the gold finger region of the circuit board is realized steps of the method.

One or more technical solutions provided by the present invention have at least the following technical effects or advantages: the present invention can automatically detect by computer by locating the golden finger area on the printed circuit board, and then analyzing the key area of the image obtained after the positioning The defects existing in the gold finger area of the printed circuit board are detected, and the problem of unstable and inaccurate detection results obtained by manual visual inspection of the gold finger area with similar characteristics is avoided, and it has strong practicability.

Description of drawings

The accompanying drawings described herein are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the present invention, but do not constitute a limitation to the embodiments of the present invention;

Fig. 1 is a schematic diagram of the defect detection process flow of the golden finger region in the present invention;

Fig. 2 is the schematic diagram of the target detection result of golden finger area in the present invention;

3 is a schematic diagram of the detection result of the remaining copper defect in the gold finger region in the present invention;

FIG. 4 is a schematic diagram of a gold finger region defect detection system according to the present invention.

Detailed ways

In order to understand the above objects, features and advantages of the present invention more clearly, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments may be combined with each other under the condition that they do not conflict with each other.

Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways that are different from the scope of this description. Therefore, the protection scope of the present invention is not subject to the following disclosure. The limitations of the specific embodiment.

Example 1

Referring to FIG. 1, the present invention provides a method for detecting defects in the gold finger region of a circuit board, including the following steps:

Obtain an image of the circuit to be tested;

Identifying the image of the circuit to be tested, obtaining the coordinates of the target area, and segmenting the image of the circuit to be tested according to the coordinates of the target area to obtain an image of the golden finger area;

processing the image of the golden finger area to obtain image data;

The image data is analyzed to obtain defect detection results.

In Embodiment 1, the obtaining of the coordinates of the target area is realized by a deep learning-based target detection framework, and the obtaining of the coordinates of the target area of the circuit under test includes the following steps:

Obtaining a plurality of circuit sample diagrams, marking the golden finger area in the circuit sample diagrams, and obtaining a first training set;

Train a first target detection framework based on the first training set to obtain a first target detection model;

The first target detection model is used to identify the image of the circuit to be tested, and the coordinates of the target detection area are obtained.

Among them, the target detection framework can be RCNN series, YOLO series and SSD series framework, RCNN series target detection framework includes RCNN, Fast-RCNN and Faster-RCNN framework, Faster-RCNN target detection framework The four steps, namely candidate region generation, feature extraction, classifier classification and regressor regression, are all handed over to the deep neural network, and all the four steps are run on the GPU, which greatly improves the operation efficiency. Therefore, In this embodiment, the Faster-RCNN target detection framework is preferred, and combined with the FPN structure, it can be effectively applied to the target detection problem with a small sample size.

Wherein, the processing of the golden finger area image includes performing grayscale processing, binarization processing, filtering processing, etc. on the image, and the specific processing method is determined according to the type of defects that may exist in the golden finger area image, This embodiment is not limited herein.

Embodiment 2

Referring to FIG. 1 , the present invention provides a method for detecting defects in the gold finger area of a circuit board. On the basis of Embodiment 1, for the remaining copper defects in the gold finger area of the printed circuit board, the The following steps are also included before processing:

Obtaining a plurality of circuit defect sample maps, marking the defect positions and categories in the circuit defect sample maps, and obtaining a second training set;

Train a second target detection framework based on the second training set to obtain a second target detection model;

The processing of the golden finger area image to obtain image data includes the following steps:

The second target detection model is used to identify the image of the golden finger area, and an image recognition result is obtained, and the image recognition result is shown in FIG. 3 .

Among them, the target detection framework can be RCNN series, YOLO series and SSD series framework, RCNN series target detection framework includes RCNN, Fast-RCNN and Faster-RCNN framework, Faster-RCNN target detection framework The four steps, namely candidate region generation, feature extraction, classifier classification and regressor regression, are all handed over to the deep neural network, and all the four steps are run on the GPU, which greatly improves the operation efficiency. Therefore, In this embodiment, the Faster-RCNN target detection framework is preferred, and combined with the FPN structure, it can be effectively applied to the target detection problem with a small sample size.

Embodiment 3

Referring to FIG. 1 , the present invention provides a method for detecting defects in the gold finger area of a circuit board. On the basis of Embodiment 1, for the contamination defect in the gold finger area of the printed circuit board, the Processing includes the following steps:

Perform binarization processing on the golden finger area image to obtain a binarized image;

Filtering the binarized image to obtain a first image;

detecting connected regions in the first image;

calculating the area of each connected region in the first image;

The analyzing the image data to obtain the defect detection result includes the following steps:

The size of the area of the connected area is judged, and if the area of the connected area is smaller than a standard value, the image of the golden finger area is defective.

Wherein, the standard value of the area of the connected region is determined according to the design layout of the circuit to be tested in the actual detection, which is not limited in this embodiment.

The binarization process is to find a suitable grayscale threshold, and set the grayscale values of all pixels in the image to 0 or 255 according to the threshold. The threshold selection methods include bimodal method, P parameter method, maximum Inter-class variance method, etc., since the circuit image to be tested is usually collected under an industrial fixed light source, the image of the golden finger region obtained by segmenting the circuit image to be tested is an image with a more regular grayscale distribution, preferably The grayscale threshold is calculated by the bimodal method.

The detection of the connected region and the calculation of the area of the connected region may be implemented by the Two-Pass algorithm or the Seed-Filling algorithm, which is not limited in this embodiment.

Among them, the filtering processing methods include mean filtering, box filtering, Gaussian filtering, morphological opening operation and morphological closing operation, etc. The morphological opening operation performs an erosion operation on the binarized image, and then performs an erosion operation on the binarized image. After the image is subjected to expansion operation to realize filtering processing, it can effectively remove small points, burrs and small bridges in the image, and keep the overall shape of the image unchanged. The morphological opening operation is preferred for image filtering processing.

Embodiment 4

Referring to FIG. 1, the present invention provides a method for detecting defects in the gold finger area of a circuit board. On the basis of Embodiment 1, for metal oxidation defects in the gold finger area of the printed circuit board, the Processing includes the following steps:

performing grayscale processing on the golden finger region image to obtain a grayscale image;

Filtering the grayscale image to obtain a second image;

calculating the grayscale average value of the second image to obtain second image data;

The analyzing the image data to obtain the defect detection result includes the following steps:

Comparing the size of the second image data with that of the standard image grayscale interval, if the second image data is not within the standard image grayscale interval, the golden finger area image is defective.

Wherein, the standard image grayscale interval is determined by specific needs during actual detection, which is not limited in this embodiment.

Among them, the filtering processing methods include mean filtering, box filtering, Gaussian filtering, morphological opening operation and morphological closing operation, etc. The morphological opening operation performs an erosion operation on the binarized image, and then performs an erosion operation on the binarized image. After the image is subjected to expansion operation to realize filtering processing, it can effectively remove small points, burrs and small bridges in the image, and keep the overall shape of the image unchanged. The morphological opening operation is preferred for image filtering processing.

The grayscale processing includes component method, maximum value method, and weighted average method. The weighted average method calculates the gray value of each pixel by weighting the R, G, and B components of the color RGB image with different weights. , it is preferable to perform grayscale processing on the golden finger area image by a weighted average method, and the weight is determined according to the three-component features of R, G, and B of the actual image, which is not limited in this embodiment.

Further, due to the presence of metal oxidation defects in the gold finger area, the original golden area becomes dark gold. Since the captured image is an RGB image, for the RGB image, the gold and dark gold are mainly in the R channel component and the G channel component of the image. In order to avoid the influence of the B channel component on the average gray level of the image, the B channel component of the image can be compensated, so the circuit to be tested is photographed under blue light to obtain the image of the circuit to be tested.

Further, in order to avoid the influence of the B channel component on the average grayscale value of the image, the B channel component of the image may also be excluded when performing grayscale processing on the golden finger area image. Grayscale processing includes the following steps:

obtaining the R component and the G component of the image pixel of the golden finger area;

Weighted calculation is performed on the R component and the G component to obtain a pixel gray value;

A grayscale image is generated from the pixel grayscale values.

Embodiment 5

Embodiment 5 of the present invention provides a circuit board gold finger region defect detection device, including a memory, a processor, and a computer program stored in the memory and running on the processor, the processor executing the The computer program realizes the steps of detecting the defects in the gold finger area of the circuit board.

Embodiment 6

Embodiment 6 of the present invention provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, implements the steps of the method for detecting defects in a gold finger region of a circuit board.

The processor may be a central processing unit (CPU, Central Processing Unit), or other general-purpose processors, digital signal processors (digital signal processors), application specific integrated circuits (Application Specific Integrated Circuits), and off-the-shelf programmable processors. Field programmable gate array or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory can be used to store the computer program and/or module, and the processor implements various functions of the circuit board gold finger region defect detection device in the invention by running or executing the data stored in the memory. The memory may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.) and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory such as hard disks, internal memory, plug-in hard disks, smart memory cards, secure digital cards, flash memory cards, at least one magnetic disk storage device, flash memory devices, or other volatile solid-state storage devices.

If the circuit board gold finger area defect detection device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the methods of the above embodiments, and can also be stored in a computer-readable storage medium through a computer program. When the computer program is executed by a processor, the above method embodiments can be implemented. A step of. Wherein, the computer program includes computer program code, object code form, executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, dot carrier signal , telecommunication signals, and software distribution media. It should be noted that, the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

The basic concepts of the present invention have been described. Obviously, for those skilled in the art, the above detailed disclosure is merely an example, and does not constitute a limitation of the present specification. Although not explicitly described herein, various modifications, improvements, and corrections to this specification may occur to those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

Meanwhile, the present specification uses specific words to describe the embodiments of the present specification. Such as "one embodiment," "an embodiment," and/or "some embodiments" means a certain feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places in this specification are not necessarily referring to the same embodiment . Furthermore, certain features, structures or characteristics of the one or more embodiments of this specification may be combined as appropriate.

Furthermore, those skilled in the art will appreciate that aspects of this specification may be illustrated and described in several patentable categories or situations, including any new and useful process, machine, product, or combination of matter, or combinations of them. of any new and useful improvements. Accordingly, various aspects of this specification may be performed entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software. The above hardware or software may be referred to as a "data block", "module", "engine", "unit", "component" or "system". Furthermore, aspects of this specification may be embodied as a computer product comprising computer readable program code embodied in one or more computer readable media.

A computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on baseband or as part of a carrier wave. The propagating signal may take a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination. Computer storage media can be any computer-readable media other than computer-readable storage media that can communicate, propagate, or transmit a program for use by coupling to an instruction execution system, apparatus, or device. Program code on a computer storage medium may be transmitted over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.

The computer program coding required for the operation of the various parts of this manual may be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python etc., conventional procedural programming languages such as C language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages, etc. The program code may run entirely on the user's computer, or as a stand-alone software package on the user's computer, or partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or to an external computer (eg, through the Internet), or in a cloud computing environment, or as a service Use eg software as a service (SaaS).

Furthermore, unless explicitly stated in the claims, the order of processing elements and sequences described in this specification, the use of alphanumerics, or the use of other names is not intended to limit the order of the processes and methods of this specification. While the foregoing disclosure discusses by way of various examples some embodiments of the invention that are presently believed to be useful, it is to be understood that such details are for purposes of illustration only and that the appended claims are not limited to the disclosed embodiments, but rather The requirements are intended to cover all modifications and equivalent combinations falling within the spirit and scope of the embodiments of this specification. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described systems on existing servers or mobile devices.

Similarly, it should be noted that, in order to simplify the expressions disclosed in this specification and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, various features may sometimes be combined into one embodiment, in the drawings or descriptions thereof. However, this method of disclosure does not imply that the subject matter of the description requires more features than are recited in the claims. Indeed, there are fewer features of an embodiment than all of the features of a single embodiment disclosed above.

For each patent, patent application, patent application publication, and other material, such as article, book, specification, publication, document, etc., cited in this specification, the entire contents of which are hereby incorporated by reference into this specification are hereby incorporated by reference. Application history documents that are inconsistent with or conflict with the contents of this specification are excluded, as are documents (currently or hereafter appended to this specification) limiting the broadest scope of the claims of this specification. It should be noted that, if there is any inconsistency or conflict between the descriptions, definitions and/or use of terms in the accompanying materials of this specification and the contents of this specification, the descriptions, definitions and/or use of terms in this specification shall prevail .

Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be construed to include the preferred embodiment and all changes and modifications that fall within the scope of the present invention.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (12)

1. a circuit board gold finger area defect detection method, is characterized in that, comprises the following steps: Obtain an image of the circuit to be tested; Identifying the image of the circuit to be tested, obtaining the coordinates of the target area, and segmenting the image of the circuit to be tested according to the coordinates of the target area to obtain an image of the golden finger area; processing the image of the golden finger area to obtain image data; The image data is analyzed to obtain defect detection results. 2. The method for detecting defects in the gold finger region of a circuit board according to claim 1, wherein the coordinates of the target region are obtained by the following steps: Obtaining a plurality of circuit sample diagrams, marking the golden finger area in the circuit sample diagrams, and obtaining a first training set; Train a first target detection framework based on the first training set to obtain a first target detection model; The first target detection model is used to identify the image of the circuit to be tested to obtain the coordinates of the target area. 3. The method for detecting defects in the gold finger area of a circuit board according to claim 1, wherein before the processing of the image of the gold finger area, the method further comprises the following steps: Obtaining a plurality of circuit defect sample maps, marking the defect positions and categories in the circuit defect sample maps, and obtaining a second training set; Train a second target detection framework based on the second training set to obtain a second target detection model; The processing of the golden finger area image to obtain image data includes the following steps: The second target detection model is used to identify the golden finger region image to obtain an image identification result. 4 . The method for detecting defects in the gold finger region of a circuit board according to claim 1 , wherein the processing of the gold finger region image comprises the following steps: 5 . Perform binarization processing on the golden finger area image to obtain a binarized image; Filtering the binarized image to obtain a first image; detecting connected regions in the first image; calculating the area of each connected region in the first image; The analyzing the image data to obtain the defect detection result includes the following steps: The size of the area of the connected area is judged, and if the area of the connected area is smaller than a standard value, the image of the golden finger area is defective. 5 . The method for detecting a defect in a gold finger region of a circuit board according to claim 4 , wherein the filtering process is to perform a morphological opening operation on the binarized image. 6 . 6. The method for detecting defects in the gold finger region of a circuit board according to claim 1, wherein the processing of the gold finger region image comprises the following steps: performing grayscale processing on the golden finger region image to obtain a grayscale image; Filtering the grayscale image to obtain a second image; calculating the grayscale average value of the second image to obtain second image data; The analyzing the image data to obtain the defect detection result includes the following steps: Comparing the size of the second image data with the grayscale interval of the standard image, if the second image data is not within the grayscale interval of the standard image, the image of the golden finger area is defective. 7 . The method for detecting defects in the gold finger region of a circuit board according to claim 6 , wherein the circuit to be tested is photographed under blue light to obtain an image of the circuit to be tested. 8 . 8 . The method for detecting defects in the gold finger region of a circuit board according to claim 6 , wherein the gold finger region image is an RGB image, and the grayscale processing for the gold finger region image comprises the following steps: 9 . step: obtaining the R component and the G component of the image pixel of the golden finger area; Weighted calculation is performed on the R component and the G component to obtain a pixel gray value; A grayscale image is generated from the pixel grayscale values. 9 . The method for detecting defects in the gold finger region of a circuit board according to claim 2 , wherein the target detection framework is a Faster-RCNN target detection framework combined with an FPN structure. 10 . 10. A circuit board gold finger region defect detection system, wherein the system comprises: an image obtaining unit, used to obtain an image of the circuit to be tested; an area identification unit, configured to identify the image of the circuit to be tested, obtain the coordinates of the target area, and segment the image of the circuit to be tested according to the coordinates of the target area to obtain an image of the golden finger area; a processing unit, configured to process the image of the golden finger area to obtain image data; An analysis unit, configured to analyze the image data to obtain defect detection results. 11. A circuit board gold finger region defect detection device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer In the program, the steps of the method for detecting defects in the gold finger region of a circuit board according to any one of claims 1-8 are implemented. 12. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, characterized in that, when the computer program is executed by a processor, the circuit board golden finger according to any one of claims 1-8 is implemented Steps of an area defect detection method.

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