YOLO-based PCB welding defect rapid identification method

Abstract

The invention relates to the technical field of PCB welding defect detection, in particular to a rapid PCB welding defect identification method based on YOLO, which comprises the following steps: acquiring PCB welding image data, extracting local texture complexity and edge gradient strength of welding area feature points, and constructing an initial welding feature distribution diagram; the welding area feature points are connected through spatial correlation attributes, and a target welding feature distribution diagram is obtained; performing fine adjustment on the initial YOLO model through the feature vector to obtain a target YOLO model; and extracting current welding area feature points and spatial correlation attributes from a PCB welding image to be detected, inputting the target YOLO model to obtain current feature vectors and target welding area feature points, and feeding back the current feature vectors and the target welding area feature points to a user. According to the method, the speed and accuracy of PCB welding defect detection can be remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing and machine vision, and specifically is a method for quickly identifying PCB welding defects based on YOLO. Background Art

[0002] In the field of electronic product manufacturing, printed circuit board (PCB) welding defect detection is a key link to ensure product quality. Traditional detection methods, such as manual visual inspection, are still widely used in some cases, but they have significant problems such as slow detection speed and low accuracy, which makes it difficult to meet the needs of large-scale production. With the continuous advancement of artificial intelligence technology, especially the widespread application of deep learning algorithms in the field of image recognition, a PCB welding defect rapid identification method based on YOLO (You Only Look Once) has emerged. The YOLO algorithm has shown great potential in target detection tasks due to its high efficiency and accuracy. This method realizes automatic identification and positioning of PCB welding defects by training a deep neural network model, greatly improving the detection efficiency and accuracy. However, some existing defect detection methods based on deep learning still have problems such as complex models, high computing resource requirements, and poor real-time performance in practical applications. Therefore, the development of a PCB welding defect rapid identification method based on the YOLO algorithm can not only overcome the shortcomings of traditional methods, but also achieve efficient and real-time defect detection while ensuring high accuracy, which has important practical significance and application value. Summary of the invention

[0003] The present invention provides a YOLO-based PCB welding defect rapid identification method, system and computer-readable storage medium, the main purpose of which is to solve the problems of slow detection speed and low accuracy in traditional PCB welding defect detection methods.

[0004] To achieve the above-mentioned purpose, the present invention provides a YOLO-based PCB welding defect rapid identification method, comprising: acquiring PCB welding image data, sequentially extracting welding area feature points from the PCB welding image data, identifying the local texture complexity and edge gradient strength of the welding area feature points, wherein the welding area feature points are the center of the welding spot or the boundary of the welding spot; determining the characteristic position of the welding area feature points in a pre-constructed welding feature distribution map according to the local texture complexity and the edge gradient strength, and obtaining an initial welding feature distribution map; acquiring spatial correlation attributes between welding area feature points in the PCB welding image data, and correlating and connecting the welding area feature points in the initial welding feature distribution map using the spatial correlation attributes to obtain a target welding feature distribution map; acquiring a feature vector between two welding area feature points in the target welding feature distribution map, and performing correlation connection on the pre-constructed initial welding feature distribution map according to the feature vector, the welding area feature points and the spatial correlation attributes. The YOLO model is fine-tuned to obtain a target YOLO model; a PCB welding image to be detected is obtained, and it is determined whether there is a welding area feature point in the PCB welding image; if there is no welding area feature point in the PCB welding image, a pre-welding image set containing the PCB welding image is obtained, and a current welding area feature point is extracted from the pre-welding image set; if there is a welding area feature point in the PCB welding image, the current welding area feature point is extracted from the PCB welding image; the current spatial correlation attribute of the current welding area feature point is extracted from the PCB welding image, and the current welding area feature point and the current spatial correlation attribute are input into the target YOLO model to obtain a current feature vector; the current feature position of the current welding area feature point is obtained, and the target welding area feature point is indexed in the target welding feature distribution map according to the current feature position and the current feature vector, and the target welding area feature point is fed back to the user.

[0005] Optionally, determining the characteristic position of the welding area feature point in a pre-constructed welding feature distribution map according to the local texture complexity and the edge gradient strength to obtain an initial welding feature distribution map includes: taking the local texture complexity as a horizontal coordinate point in the welding feature distribution map; taking the edge gradient strength as a vertical coordinate point in the welding feature distribution map; determining the characteristic position of the welding area feature point in the welding feature distribution map according to the horizontal coordinate point and the vertical coordinate point; and calibrating the characteristic positions of all welding area feature points in the welding feature distribution map to obtain the initial welding feature distribution map.

[0006] Optionally, the acquiring of spatial correlation attributes between welding area feature points in the PCB welding image data, and correlating and connecting the welding area feature points in the initial welding feature distribution map using the spatial correlation attributes to obtain a target welding feature distribution map includes: acquiring a set of associated welding area feature points associated with the welding area feature points; sequentially extracting associated welding area feature points from the associated welding area feature point set, and identifying spatial correlation attributes between the welding area feature points and the associated welding area feature points; and correlating and connecting the welding area feature points with the associated welding area feature points using the spatial correlation attributes to obtain the target welding feature distribution map.

[0007] Optionally, obtaining the feature vector between two welding area feature points in the target welding feature distribution map includes: obtaining feature positions of two welding area feature points that are correlated and connected in the target welding feature distribution map; and calculating the feature vector between the two welding area feature points according to a pre-constructed feature vector calculation formula using the feature positions.

[0008] Optionally, the characteristic vector calculation formula is as follows: ,in, Represents the feature vector from the feature point of the welding area to the feature point of the welding area, Indicates the characteristic position of the characteristic points in the welding area, Indicates the characteristic position of the characteristic points of the associated welding area, represents the transverse unit vector, represents the longitudinal unit vector.

[0009] Optionally, the determining whether there are welding area feature points in the PCB welding image includes: removing preset background noise and interference areas in the PCB welding image to obtain an initial welding area candidate set; determining whether there are welding area feature points in the initial welding area candidate set; if there are welding area feature points in the initial welding area candidate set, determining that there are welding area feature points in the PCB welding image; if there are no welding area feature points in the initial welding area candidate set, determining that there are no welding area feature points in the PCB welding image.

[0010] Optionally, acquiring a preceding welding image set including the PCB welding image, and extracting a current welding area feature point from the preceding welding image set, includes: extracting a first preceding welding image of the PCB welding image from the preceding welding image set; judging whether there is a welding area feature point in the first preceding welding image; if there is no welding area feature point in the first preceding welding image, extracting preceding welding images from the preceding welding image set in reverse order until there is a welding area feature point in the preceding welding image, and using the welding area feature point as the current welding area feature point; if there is a welding area feature point in the first preceding welding image, extracting the current welding area feature point in the first preceding welding image.

[0011] Optionally, indexing the target welding area feature point in the target welding feature distribution map according to the current feature position and the current feature vector includes: taking the current feature position as the vector starting point of the current feature vector to obtain an indexed feature vector; identifying the vector end point of the indexed feature vector, and identifying the welding area feature point with the same feature position as the vector end point; and taking the welding area feature point with the same feature position as the vector end point as the target welding area feature point.

[0012] Optionally, the method further comprises: determining whether there is a current spatial correlation attribute matching the current welding area feature point in the PCB welding image; if there is no current spatial correlation attribute matching the current welding area feature point in the PCB welding image, continuing to extract the current welding area feature point in the previous welding image set until a current welding area feature point matching the current spatial correlation attribute is obtained, and extracting the current spatial correlation attribute of the current welding area feature point in the PCB welding image; if there is a current spatial correlation attribute matching the current welding area feature point in the PCB welding image, extracting the current spatial correlation attribute of the current welding area feature point in the PCB welding image.

[0013] In order to solve the above problems, the present invention also provides a PCB welding defect rapid identification system based on YOLO, the system comprising: an initial welding feature distribution map construction module, used to obtain PCB welding image data, sequentially extract welding area feature points in the PCB welding image data, identify the local texture complexity and edge gradient strength of the welding area feature points, wherein the welding area feature points are the welding point center or the welding point boundary; determine the characteristic position of the welding area feature points in the pre-constructed welding feature distribution map according to the local texture complexity and edge gradient strength, and obtain the initial welding feature distribution map; a target welding feature distribution map construction module, used to obtain the spatial correlation attributes between the welding area feature points in the PCB welding image data, and use the spatial correlation attributes to correlate the welding area feature points in the initial welding feature distribution map to obtain the target welding feature distribution map; a feature vector association relationship establishment module, used to obtain the feature vector between two welding area feature points in the target welding feature distribution map, and according to the feature vector, the welding area feature points and the spatial correlation The pre-built initial YOLO model is fine-tuned by using the associated attributes to obtain a target YOLO model; a current welding area feature point extraction module is used to obtain a PCB welding image to be detected and determine whether there is a welding area feature point in the PCB welding image; if there is no welding area feature point in the PCB welding image, a pre-welding image set containing the PCB welding image is obtained, and the current welding area feature point is extracted from the pre-welding image set; if there is a welding area feature point in the PCB welding image, the current welding area feature point is extracted from the PCB welding image; a target welding area feature point feedback module is used to extract the current spatial correlation attribute of the current welding area feature point in the PCB welding image, input the current welding area feature point and the current spatial correlation attribute into the target YOLO model, and obtain a current feature vector; obtain the current feature position of the current welding area feature point, index the target welding area feature point in the target welding feature distribution map according to the current feature position and the current feature vector, and feed back the target welding area feature point to the user.

[0014] In order to solve the above problems, the present invention also provides an electronic device, the electronic device comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned YOLO-based PCB welding defect rapid identification method. In order to solve the above problems, the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by the processor in the electronic device to implement the above-mentioned YOLO-based PCB welding defect rapid identification method.

[0015] Compared with the background technology: the traditional PCB welding defect detection method has the phenomenon of slow detection speed and low accuracy. In the process of extracting the feature points of the target welding area, the embodiment of the present invention needs to first construct a target welding feature distribution map, so as to achieve the purpose of indexing the feature points of the target welding area in the target welding feature distribution map according to the current feature position and the current feature vector, and the current feature position refers to the welding area feature point in the PCB welding image to be detected. When constructing the target welding feature distribution map, the local texture complexity and edge gradient strength of the welding area feature point are first identified, and then the local texture complexity and edge gradient strength are used to determine the feature position of the welding area feature point in the target welding feature distribution map. The feature position relationship between the two welding area feature points in the feature distribution map is calculated, and the pre-built initial YOLO model is fine-tuned according to the feature vector, the welding area feature points and the spatial correlation attributes to obtain the target YOLO model. The indexing efficiency of the target welding area feature points is greatly simplified by establishing the feature vector. At this time, the current welding area feature points and the corresponding current spatial correlation attributes can be extracted from the PCB welding image to be detected or the front welding image set, and the current feature position and the current feature vector are determined according to the current welding area feature points and the corresponding current spatial correlation attributes. Finally, the target welding area feature points are indexed in the target welding feature distribution map according to the current feature position and the current feature vector and fed back to the user. Therefore, the YOLO-based PCB welding defect rapid identification method, system, electronic device and computer-readable storage medium proposed in the present invention can solve the problems of slow detection speed and low accuracy of traditional PCB welding defect detection methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for rapid identification of PCB welding defects based on YOLO provided in an embodiment of the present invention shows the overall steps from acquiring PCB welding image data to feeding back feature points of the target welding area.

[0017] Figure 2 It is a schematic diagram of the construction process of the initial welding feature distribution map and the target welding feature distribution map in an embodiment of the present invention, and describes in detail the application of local texture complexity, edge gradient intensity and spatial correlation attributes in the feature distribution map.

[0018] Figure 3 A schematic diagram of the module structure of a YOLO-based PCB welding defect rapid identification system provided in an embodiment of the present invention includes an initial welding feature distribution map construction module, a target welding feature distribution map construction module, a feature vector association relationship establishment module, a current welding area feature point extraction module and a target welding area feature point feedback module. DETAILED DESCRIPTION

[0019] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0020] The present invention provides a method, system and computer-readable storage medium for rapid identification of PCB welding defects based on YOLO. The core of the invention is to realize efficient detection of PCB welding defects by constructing a target welding feature distribution map and combining the YOLO model. Figures 1 to 3 In practical applications, it is first necessary to obtain PCB welding image data, which usually comes from high-resolution cameras or scanning equipment on industrial production lines. Figure 1 As shown in the figure, the first step of the process is to extract the characteristic points of the welding area and identify the local texture complexity and edge gradient strength of these characteristic points. The local texture complexity reflects the detailed information of the surface of the weld, while the edge gradient strength is used to characterize the changes in the boundary of the weld. In order to facilitate subsequent processing, the local texture complexity is used as the horizontal coordinate point in the welding feature distribution map, and the edge gradient strength is used as the vertical coordinate point. Based on these two parameters, the specific position of each welding area characteristic point in the welding feature distribution map can be determined. For example, assuming that the local texture complexity of a welding area characteristic point is 0.75 and the edge gradient strength is 0.6, its position in the welding feature distribution map is (0.75, 0.6). After the position calibration of all welding area characteristic points is completed, the initial welding feature distribution map can be generated.

[0021] Next, it is necessary to further construct the target welding feature distribution map. The key to this process is to analyze the spatial correlation attributes between the characteristic points in the welding area. Figure 2 As shown, firstly, a set of associated welding area feature points associated with a certain welding area feature point is obtained. These associated feature points are usually welds that are physically adjacent to the current feature point or have a certain logical relationship. Subsequently, each associated feature point is extracted in turn from the associated welding area feature point set, and their spatial association attributes with the current feature point are calculated. Spatial association attributes can be defined in a variety of ways, such as the Euclidean distance, direction angle, or relative position relationship between two points. Using these spatial association attributes, the welding area feature points in the initial welding feature distribution map can be relatedly connected to form a target welding feature distribution map. This connection can not only reflect the geometric relationship between the welds, but also provide a basis for the subsequent feature vector calculation.

[0022] Based on the target welding feature distribution map, the feature vector between the feature points of two welding areas is further calculated. Figure 2As shown, the calculation formula of the eigenvector is ,in and They represent the characteristic positions of the characteristic points of the welding area and the characteristic points of the associated welding area, and are the transverse and longitudinal unit vectors respectively. For example, if the characteristic positions of the two characteristic points of the welding area are (0.75, 0.6) and (0.85, 0.7), the characteristic vector between them is In this way, the relative position relationship between the feature points in the welding area can be quantified, providing an important basis for subsequent model fine-tuning.

[0023] After completing the calculation of the feature vector, it is necessary to fine-tune the pre-built initial YOLO model to obtain the target YOLO model. The initial YOLO model is a target detection algorithm based on deep learning, which is characterized by being able to simultaneously predict the category and position of the target in a single forward propagation. In the present invention, the input of the initial YOLO model includes welding area feature points, feature vectors, and spatial correlation attributes. By training these input data, the model can be better adapted to the PCB welding defect detection task. During the fine-tuning process, the back propagation algorithm is used to optimize the model parameters until the performance of the model on the verification set reaches the expected level. The target YOLO model finally obtained has a higher detection speed and accuracy, which can meet the real-time requirements in industrial production.

[0024] In the actual detection stage, it is first necessary to obtain the PCB welding image to be detected and determine whether there are welding area feature points in it. Figure 1 As shown in the figure, this process includes removing background noise and interference areas to obtain an initial welding area candidate set. Background noise may come from uneven lighting or equipment jitter during image acquisition, while interference areas may be other non-welding components on the PCB board. These interference factors can be effectively removed through image preprocessing techniques, such as Gaussian filtering, morphological operations, etc. Subsequently, the initial welding area candidate set is analyzed. If there are welding area feature points, it is determined that the PCB welding image contains welding area feature points; otherwise, it is determined that there are no welding area feature points.

[0025] If there are no welding area feature points in the PCB welding image to be inspected, it is necessary to extract the current welding area feature points from the previous welding image set. The previous welding image set refers to a series of images collected before the current image, and these images are usually arranged in chronological order. Figure 1As shown, firstly, the first pre-welding image in the pre-welding image set is extracted, and it is determined whether there is a welding area feature point in it. If there is no welding area feature point in the first pre-welding image, the pre-welding images are extracted in reverse order until an image containing a welding area feature point is found, and the image is used as the current welding area feature point. If there is already a welding area feature point in the first pre-welding image, the feature point is directly extracted as the current welding area feature point.

[0026] For PCB welding images containing welding area feature points, it is necessary to further extract the current spatial correlation attributes of the current welding area feature points. Figure 1 As shown in the figure, this process includes determining whether the current welding area feature point has a matching spatial correlation attribute. If there is no matching spatial correlation attribute in the current image, the current welding area feature point is continuously extracted from the previous welding image set until a matching attribute is found. If there is already a matching spatial correlation attribute in the current image, the attribute is directly extracted. Subsequently, the current welding area feature point and its spatial correlation attribute are input into the target YOLO model to obtain the current feature vector. The calculation method of the current feature vector is consistent with the aforementioned feature vector calculation formula.

[0027] After obtaining the current feature vector, it is necessary to index the feature points of the target welding area in the target welding feature distribution map according to the current feature position and the current feature vector. Figure 1 As shown in the figure, this process includes taking the current feature position as the vector starting point of the current feature vector to obtain the index feature vector. Subsequently, the vector end point of the index feature vector is identified, and the welding area feature point with the same feature position as the vector end point is searched in the target welding feature distribution map. Finally, the feature point is fed back to the user as the target welding area feature point. For example, if the current feature position is (0.75, 0.6) and the current feature vector is Then the vector end point of the index feature vector is (0.85, 0.7). In the target welding feature distribution map, find the welding area feature point with the feature position of (0.85, 0.7) and use it as the target welding area feature point.

[0028] In order to implement the above method, the present invention also provides a PCB welding defect rapid identification system based on YOLO. Figure 3As shown in the figure, the system includes multiple functional modules: the initial welding feature distribution map construction module is responsible for extracting welding area feature points and generating the initial welding feature distribution map; the target welding feature distribution map construction module is responsible for analyzing the spatial correlation attributes between welding area feature points and generating the target welding feature distribution map; the feature vector association relationship establishment module is responsible for calculating the feature vector and fine-tuning the initial YOLO model; the current welding area feature point extraction module is responsible for judging whether there are welding area feature points in the image to be detected and extracting the current welding area feature points; the target welding area feature point feedback module is responsible for indexing the target welding area feature points according to the current feature position and feature vector and feeding back to the user. These modules work together to achieve rapid identification of PCB welding defects.

[0029] In addition, the present invention also provides an electronic device and a computer-readable storage medium. The electronic device includes at least one processor and a memory connected to the processor in communication therewith, and the memory stores instructions that can be executed by the processor, and these instructions are used to implement the above-mentioned YOLO-based PCB welding defect rapid identification method. The computer-readable storage medium also stores at least one instruction, and when these instructions are executed by the processor in the electronic device, the same functions can be achieved. In this way, the technical solution of the present invention can be flexibly deployed on a variety of hardware platforms to meet the needs of different application scenarios.

[0030] In summary, the present invention significantly improves the speed and accuracy of PCB welding defect detection by constructing a target welding feature distribution map and combining the YOLO model. In practical applications, this method can quickly locate the characteristic points of the welding area and identify potential defects, providing strong technical support for industrial production.

Claims

1. A YOLO-based PCB welding defect rapid identification method, characterized in that: The method comprises: acquiring PCB welding image data, sequentially extracting welding area feature points from the PCB welding image data, identifying local texture complexity and edge gradient strength of the welding area feature points, wherein the welding area feature points are welding point centers or welding point boundaries; determining feature positions of the welding area feature points in a pre-constructed welding feature distribution map according to the local texture complexity and edge gradient strength, and obtaining an initial welding feature distribution map; acquiring spatial correlation attributes between welding area feature points in the PCB welding image data, and correlating and connecting the welding area feature points in the initial welding feature distribution map using the spatial correlation attributes, and obtaining a target welding feature distribution map; acquiring two welding area feature points in the target welding feature distribution map; and obtaining the welding area feature points in the pre-constructed welding feature distribution map. The pre-built initial YOLO model is fine-tuned according to the feature vector, the welding area feature points and the spatial correlation attributes to obtain a target YOLO model; the PCB welding image to be detected is obtained, and it is determined whether there are welding area feature points in the PCB welding image; the current spatial correlation attributes of the current welding area feature points are extracted from the PCB welding image, and the current welding area feature points and the current spatial correlation attributes are input into the target YOLO model to obtain a current feature vector; the current feature position of the current welding area feature point is obtained, and the target welding area feature point is indexed in the target welding feature distribution map according to the current feature position and the current feature vector, and the target welding area feature point is fed back to the user.

2. The YOLO-based PCB welding defect rapid identification method according to claim 1, characterized in that: The method of determining the characteristic position of the welding area feature point in a pre-constructed welding feature distribution map according to the local texture complexity and the edge gradient strength to obtain an initial welding feature distribution map includes: taking the local texture complexity as a horizontal coordinate point in the welding feature distribution map; taking the edge gradient strength as a vertical coordinate point in the welding feature distribution map; determining the characteristic position of the welding area feature point in the welding feature distribution map according to the horizontal coordinate point and the vertical coordinate point; and calibrating the characteristic positions of all welding area feature points in the welding feature distribution map to obtain the initial welding feature distribution map.

3. The YOLO-based PCB welding defect rapid identification method as claimed in claim 2, characterized in that: The method of acquiring spatial correlation attributes between welding area feature points in the PCB welding image data, and correlating and connecting the welding area feature points in the initial welding feature distribution map using the spatial correlation attributes to obtain a target welding feature distribution map includes: acquiring an associated welding area feature point set associated with the welding area feature points; sequentially extracting associated welding area feature points from the associated welding area feature point set, and identifying spatial correlation attributes between the welding area feature points and the associated welding area feature points; and correlating and connecting the welding area feature points with the associated welding area feature points using the spatial correlation attributes to obtain the target welding feature distribution map.

4. The YOLO-based PCB welding defect rapid identification method as claimed in claim 3, characterized in that: The method of obtaining a feature vector between two welding area feature points in the target welding feature distribution map includes: obtaining feature positions of two welding area feature points that are correlated and connected in the target welding feature distribution map; and calculating a feature vector between the two welding area feature points according to a pre-constructed feature vector calculation formula using the feature positions.

5. The YOLO-based PCB welding defect rapid identification method as claimed in claim 4, characterized in that: The characteristic vector calculation formula and the correlation vector calculation formula are as follows: ,in, Represents the feature vector from the feature point of the welding area to the feature point of the welding area, Indicates the characteristic position of the characteristic points in the welding area, Indicates the characteristic position of the characteristic points of the associated welding area, represents the transverse unit vector, represents the longitudinal unit vector.

6. The YOLO-based PCB welding defect rapid identification method according to claim 1, characterized in that: The determining whether there is a welding area feature point in the PCB welding image includes: if there is no welding area feature point in the PCB welding image, obtaining a previous welding image set including the PCB welding image, and extracting a current welding area feature point in the previous welding image set; if there is a welding area feature point in the PCB welding image, extracting the current welding area feature point in the PCB welding image; removing a preset background noise and interference area in the PCB welding image to obtain an initial welding area candidate set; determining whether there is a welding area feature point in the initial welding area candidate set; if there is a welding area feature point in the initial welding area candidate set, determining that there is a welding area feature point in the PCB welding image; if there is no welding area feature point in the initial welding area candidate set, determining that there is no welding area feature point in the PCB welding image.

7. The YOLO-based PCB welding defect rapid identification method according to claim 6, characterized in that: The step of acquiring a pre-welding image set including the PCB welding image and extracting a current welding area feature point from the pre-welding image set includes: extracting a first pre-welding image of the PCB welding image from the pre-welding image set; judging whether there is a welding area feature point in the first pre-welding image; if there is no welding area feature point in the first pre-welding image, extracting pre-welding images from the pre-welding image set in reverse order until there is a welding area feature point in the pre-welding image, and using the welding area feature point as the current welding area feature point; if there is a welding area feature point in the first pre-welding image, extracting the current welding area feature point in the first pre-welding image.

8. The YOLO-based PCB welding defect rapid identification method according to claim 1, characterized in that: The method of indexing a target welding area feature point in the target welding feature distribution map according to the current feature position and the current feature vector includes: taking the current feature position as the vector starting point of the current feature vector to obtain an index feature vector; identifying the vector end point of the index feature vector, identifying a welding area feature point whose feature position is the same as the vector end point; and taking the welding area feature point whose feature position is the same as the vector end point as the target welding area feature point.

9. The YOLO-based PCB welding defect rapid identification method according to claim 6, characterized in that: The method further comprises: determining whether there is a current spatial correlation attribute matching the current welding area feature point in the PCB welding image; if there is no current spatial correlation attribute matching the current welding area feature point in the PCB welding image, continuing to extract the current welding area feature point in the previous welding image set until a current welding area feature point matching the current spatial correlation attribute is obtained, and extracting the current spatial correlation attribute of the current welding area feature point in the PCB welding image; if there is a current spatial correlation attribute matching the current welding area feature point in the PCB welding image, extracting the current spatial correlation attribute of the current welding area feature point in the PCB welding image.

10. A YOLO-based PCB welding defect rapid identification system, characterized in that: The system comprises: an initial welding feature distribution map construction module, which is used to obtain PCB welding image data, sequentially extract welding area feature points from the PCB welding image data, identify local texture complexity and edge gradient strength of the welding area feature points, wherein the welding area feature points are welding point centers or welding point boundaries; determine feature positions of the welding area feature points in a pre-constructed welding feature distribution map according to the local texture complexity and edge gradient strength, and obtain an initial welding feature distribution map; a target welding feature distribution map construction module, which is used to obtain spatial correlation attributes between welding area feature points in the PCB welding image data, and use the spatial correlation attributes to correlate the welding area feature points in the initial welding feature distribution map to obtain a target welding feature distribution map; a feature vector correlation relationship establishment module, which is used to obtain a feature vector between two welding area feature points in the target welding feature distribution map, and micro-connect the pre-constructed initial YOLO model according to the feature vector, welding area feature points and spatial correlation attributes. to obtain a target YOLO model; a current welding area feature point extraction module, used to obtain a PCB welding image to be detected, and determine whether there is a welding area feature point in the PCB welding image; if there is no welding area feature point in the PCB welding image, a pre-welding image set containing the PCB welding image is obtained, and the current welding area feature point is extracted from the pre-welding image set; if there is a welding area feature point in the PCB welding image, the current welding area feature point is extracted from the PCB welding image; a target welding area feature point feedback module, used to extract the current spatial correlation attribute of the current welding area feature point in the PCB welding image, input the current welding area feature point and the current spatial correlation attribute into the target YOLO model, and obtain a current feature vector; obtain the current feature position of the current welding area feature point, index the target welding area feature point in the target welding feature distribution map according to the current feature position and the current feature vector, and feed back the target welding area feature point to the user.

Images