CN117422681A - PCB surface defect detection method based on improved YOLOv8 algorithm - Google Patents

PCB surface defect detection method based on improved YOLOv8 algorithm Download PDF

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CN117422681A
CN117422681A CN202311378097.5A CN202311378097A CN117422681A CN 117422681 A CN117422681 A CN 117422681A CN 202311378097 A CN202311378097 A CN 202311378097A CN 117422681 A CN117422681 A CN 117422681A
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pcb
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左健存
常远培
薛颖
张宇
孙晶国
季张源
李和威
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Shanghai Polytechnic University
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Abstract

The invention discloses a PCB surface defect detection method based on an improved YOLOv8 algorithm; the method adopts an improved model based on YOLOv8 to detect defects; in the model, an up-sampling process is added in a Yolov8 Neck network, original FPN and PAN structures are replaced by BiFPN structures, and a multi-level feature network is obtained by means of weighted feature fusion and cross-scale connection; providing a C2f-CAM module, wherein the module fuses a CAM attention mechanism into the C2f module, and replaces the C2f module in the up-sampling process in a Neck network with the C2f-CAM module; meanwhile, a loss function is optimized based on the normalized Wasserstein distance is introduced during model training; the method can improve the detection precision of the surface defects of the PCB, has higher accuracy and recall rate, and solves the problem of poor effect of the YOLOv8 algorithm in the detection process of the small target.

Description

PCB surface defect detection method based on improved YOLOv8 algorithm
Technical Field
The invention belongs to the technical field of printed circuit board defect detection, and particularly relates to a PCB surface defect detection method based on an improved YOLOv8 algorithm.
Background
The PCB (Printed Circuit Board ) is an important component in electronic devices for enabling the connection and support of electronic components. Surface defects such as short circuits, open circuits, soldering problems, etc. may cause circuit failures and performance degradation during PCB manufacturing. Therefore, the surface defect detection of the PCB is important to improve the quality and reliability of the electronic product.
In recent years, deep learning technology has made remarkable progress in the field of computer vision and has made remarkable results in the task of target detection. YOLO (You Only Look Once) is a real-time target detection algorithm based on deep learning, which is receiving a great deal of attention at high speed and accuracy. YOLOv8 is the latest version in the YOLO series algorithm. However, the conventional YOLOv8 algorithm has poor detection effect on small targets.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the PCB surface defect detection method based on the improved YOLOv8 algorithm, which improves the detection precision of the PCB surface defect, improves the YOLOv8 target detection algorithm aiming at the conventional scale target, realizes effective detection of the PCB surface defect, and has higher accuracy and recall rate.
The invention provides a PCB surface defect detection method based on an improved YOLOv8 algorithm. According to the method, an up-sampling process is increased by improving a feature fusion network, a weighted bidirectional feature pyramid network (Bi-directional Feature Pyramid Network, biFPN) is introduced, a multi-level feature network is obtained by utilizing weighted feature fusion and cross-scale connection, and the detection efficiency of a small target is improved. The invention also provides a C2f-CAM module, which fuses a context information attention mechanism (Context Augmentation Module, CAM) into the original C2f module of the YOLOv8, selects more useful context information and extracts finer and meaningful features. Meanwhile, a measurement method of normalized Wasserstein distance (Normalized Wasserstein Distance, NWD) is introduced, the position, shape and visual similarity of the target frame are comprehensively considered, and the accuracy of similarity measurement is improved. The technical scheme of the invention is as follows.
The invention provides a PCB surface defect detection method based on an improved YOLOv8 algorithm, which comprises the following steps:
step one: performing data enhancement processing on the disclosed PCB data set to obtain an extended data set; dividing the extended data set into a training set and a testing set;
step two: establishing a modified YOLOv8 PCB surface defect detection model
Adding an up-sampling process in a Neck network of Yolov8, replacing the original FPN and PAN structures of the Neck network in Yolov8 with a weighted bidirectional feature pyramid network BiFPN structure, and obtaining a multi-stage feature network through the BiFPN network by utilizing weighted feature fusion and cross-scale connection; fusing a context information attention mechanism CAM into a C2f module, providing a C2f-CAM module, and replacing the C2f module in the up-sampling process in a Neck network with the C2f-CAM module; constructing a PCB surface defect detection model based on improved YOLOv8, wherein the PCB surface defect detection model comprises an input end, a back bone network, a Neck network and a detection head;
step three: training the PCB surface defect detection model based on the improved YOLOv8 in a training set sample to obtain a target detection optimal model;
step four: and taking sample data in the test set as input of a target detection optimal model, carrying out defect detection through the target detection optimal model, outputting a defect detection result, and evaluating the detection result.
In the first step of the invention, the data enhancement processing comprises random overturn, random mirror image, random brightness change and random scaling processing; proportion 8 of training set and validation set: 2.
in the second step, in the improved YOLOv8 PCB surface defect detection model, an image is sent into a backhaul network from an input end to perform feature extraction, then a Neck network performs feature fusion on the extracted features, and the fused features are sent into three detection heads with different scales to perform detection, so that a detection result is obtained.
In the second step, the Neck network in the YOLOv8 is improved, a sampling process for the shallow feature map is added, the original FPN+PAN structure is replaced by the BiFPN structure, multi-scale feature fusion is carried out through the BiFPN network, different weights are given to each layer for fusion, the network is focused on important layers, node connection of unnecessary layers is reduced, the model has better performance when detecting PCB surface defects with different scales, and the problems of small target missing detection and false detection are solved.
In the second step, the C2f-CAM module inserts the CAM attention mechanism before the last convolution layer in the C2f module, namely the convolution layer performs CAM attention conversion on the output feature map, and then the converted feature map is obtained as the input of the convolution layer, so that the convolution layer receives more useful channel information, and the expression capability and the discrimination of the feature map are improved.
In the third step, a small target detection evaluation method based on normalized Wasserstein distance, namely NWD measurement, is introduced during training. Obtaining a boundary frame of target detection, wherein the boundary frame comprises a prediction frame and a real frame, and based on the prediction frame and the real frame, a Wasserstein distance is obtained; normalizing the Wasserstein distance; the loss function of the YOLOv 8-based PCB surface defect detection model was improved according to the normalized wasperstein distance. The Loss functions of yolov8 include classification Loss (VFL Loss) and regression Loss (CIOU loss+df Loss), which are the Loss functions responsible for localization, and the present method improves CIOU Loss by NWD.
Compared with the prior art, the invention has the beneficial effects that:
the invention aims at PCB surface defect detection, improves the YOLOv8 algorithm, comprises improving a feature fusion network, fusing a context information attention mechanism and optimizing a YOLOv8 model with an improved loss function, improves the feature fusion capability, aims at the problem of poor effect in the small target detection process, improves the detection precision of the model, and increases the generalization capability and robustness of the model. The PCB surface defect detection method based on the improved YOLOv8 algorithm has higher practical value and application prospect. By the method, the efficiency and the accuracy of PCB manufacture and quality control can be improved, so that the quality and the reliability of electronic products are improved.
Drawings
FIG. 1 is a schematic diagram of a modified YOLOv8 network model.
Fig. 2 is a schematic diagram of an improved feature fusion network.
FIG. 3 is a schematic diagram of a CAM module.
FIG. 4 is a schematic diagram of a C2f-CAM module.
Detailed Description
The invention will be further described with reference to the drawings and examples, which should not be construed as limiting the scope of the invention.
Example 1
1. Data preprocessing: using the disclosed PCB defect dataset, write python code, data enhance the dataset to 4899 sheets and convert it to YOLO training format, following 8: the scale of 2 is divided into training and test sets.
2. And improving the YOLOv8 network model by improving a feature fusion network, fusing a context information attention mechanism and optimizing a loss function, and constructing a PCB surface defect detection model based on the improved YOLOv 8. The improved YOLOv8 network model structure is shown in fig. 1. The specific improvement steps can be divided into three steps:
2.1 improved feature fusion network
An up-sampling process is added, a weighted bidirectional feature pyramid network (BiFPN) is introduced into a Neck network part of a YOLOv8 model, an original Concat module is replaced by a BiFPN module, and an improved feature fusion network schematic diagram is shown in figure 2; biFPN is a feature fusion mechanism with weights, each path is assigned with a learnable weight, the weights are continuously updated through learning of the data features, so that more important information is obtained, and the network feature fusion capability is further enhanced through the learnable weights. BiFPN realizes trans-scale feature fusion through the following fusion formula:
wherein w is i Representing the weight value obtained after the network training; i i Representing the characteristics of the input.
2.2 fusion context information attention mechanism
The invention provides a C2f-CAM module, wherein the CAM attention mechanism is a neural network module based on a context information mechanism, and the CAM module consists of a space attention module and a channel attention module which are respectively used for extracting context information of space and channel dimensions, and the structure schematic diagram of the CAM module is shown in figure 3. For the design of the module, the invention adopts the CAM attention mechanism added in the original C2f module of the YOLOv8 to enhance the characteristics of the context information among different channels. The schematic structure of the C2f-CAM module is shown in fig. 4, and a CAM attention mechanism is inserted before the last convolution layer in the C2f module, that is, the convolution layer performs CAM attention transformation on the output feature map, and then the transformed feature map is used as the input of the convolution layer. This allows the convolutional layer to receive more useful channel information, improving the expressive power and discrimination of feature maps. The C2f module of the Neck network up-sampling process is replaced by a C2f-CAM module.
The C2f-CAM module can improve the detection effect on small targets, dense targets, shielding targets and other targets difficult to detect. Features with different dimensions and positions can be selected in a self-adaptive manner and are dynamically fused so as to adapt to defects with different dimensions and shapes; more useful channel information can be selected by the attention mechanism, extracting finer and meaningful features.
2.3 optimizing the loss function
The NWD is introduced to measure and optimize the loss function, and has the advantages of insensitivity to objects with different scales and suitability for measuring the similarity between tiny objects. In the defect detection process, a boundary box is modeled as two-dimensional Gaussian distribution, and the second-order Wasserstein distance between the boundary boxes is
Wherein,representing the second order Wasserstein distance, cx between bounding box A and bounding box B A Is the central abscissa, cy, of bounding box A A Is the central ordinate, w, of the bounding box A A For the width of bounding box A, h A To the height of bounding box A, cx B Is the center abscissa, cy, of the bounding box B B Is the central ordinate, w, of the bounding box B B For the width of the boundary box B, h B For the height of bounding box B, TT represents the transpose.
The second order wasperstein distance is normalized and a so-called normalized gaussian wasperstein distance is obtained:
in the formula, NWD (mu) A ,μ B ) Representing the normalized Gaussian Wasserstein distance between bounding box A and bounding box B, C is the normalization constant.
L NWD ＝1-NWD(N a ,N b )
Wherein L is NWD Is normalized Wasserstein loss, N a Is the gaussian distribution of the predicted bounding box a,N b is a gaussian distribution of a true bounding box B, NWD (N a ,N b ) Is the normalized wasperstein distance of the predicted bounding box a and the real bounding box B.
3. And (3) sending the PCB surface defect training set into the improved YOLOv8 network model for training to obtain the defect detection optimal model.
4. And taking sample data in the test set as input, carrying out target detection through the improved model, outputting a target detection result, and evaluating the detection result. The performance of the improved YOLOv8 model is objectively evaluated, the conventional accuracy (P), recall (R) and average accuracy average value mAP are used as evaluation indexes, an ablation experiment is performed on the model, and the defect detection performance results (average value of 6 defect detection performances) are shown in table 1.
Table 1 ablation experiments based on the modified YOLOv8 algorithm
Algorithm P R mAP0.5 mAP0.5:0.95
YOLOv8 98.7％ 96.1％ 98.1％ 74.0％
YOLOv8+BiFPN 99.2％ 97.3％ 98.9％ 75.8％
YOLOv8+BiFPN+C2F-CAM 99.2％ 97.5％ 99.2％ 78.7％
YOLOv8+BiFPN+C2F-CAM+NWD 99.4％ 97.8％ 99.3％ 79.0％
As can be seen from Table 1, the PCB surface defect detection model based on improved YOLOv8 provided by the invention has the advantages that the accuracy, recall rate and mAP0.5 average precision are respectively improved by 0.7%,1.7%,1.2% and 5% on the mAP0.5:0.95 average precision, the defect detection effect is obviously improved on the severe index of mAP0.5:0.95, and the model has good reference significance for PCB defect detection application.
While the present invention has been described in detail through the foregoing description of the preferred embodiment, it should be understood that the foregoing description is not to be considered as limiting the invention. Many modifications and substitutions of the present invention will become apparent to those of ordinary skill in the art upon reading the foregoing. Accordingly, the scope of the invention should be limited only by the attached claims.

Claims (5)

1. A PCB surface defect detection method based on an improved YOLOv8 algorithm is characterized by comprising the following steps:
step one: performing data enhancement processing on the disclosed PCB data set to obtain an extended data set; dividing the extended data set into a training set and a testing set;
step two: establishing a modified YOLOv8 PCB surface defect detection model
Adding an up-sampling process in a Neck network of the YOLOv8, replacing the original FPN and PAN structures of the Neck network in the YOLOv8 with a BiFPN structure, and obtaining a multi-stage feature network through the BiFPN network by utilizing weighted feature fusion and cross-scale connection; fusing a context information attention mechanism CAM into a C2f module, providing a C2f-CAM module, and replacing the C2f module in the up-sampling process in a Neck network with the C2f-CAM module; constructing a PCB surface defect detection model based on improved YOLOv8, wherein the PCB surface defect detection model comprises an input end, a back bone network, a Neck network and a detection head;
step three: training the PCB surface defect detection model based on the improved YOLOv8 in a training set sample to obtain a target detection optimal model;
step four: and taking sample data in the test set as input of a target detection optimal model, carrying out defect detection through the target detection optimal model, outputting a defect detection result, and evaluating the detection result.
2. The method for detecting surface defects of a PCB based on the modified YOLOv8 algorithm of claim 1, wherein in step one, the data enhancement process includes performing random flipping, random mirroring, random brightness variation, and random scaling; proportion 8 of training set and validation set: 2.
3. the method for detecting surface defects of a PCB based on the modified YOLOv8 algorithm of claim 1, wherein in the second step, the network of neg is modified, a sampling process for shallow feature map is added, and the FPN and PAN structures are replaced with BiFPN structures.
4. The method for detecting surface defects of a PCB based on the modified YOLOv8 algorithm of claim 1, wherein in the second step, the C2f-CAM module inserts a CAM attention mechanism before the last convolution layer in the C2f module, i.e. the convolution layer is used to perform CAM attention transformation on the output feature map, and then the transformed feature map is used as an input of the convolution layer, which enables the convolution layer to receive more useful channel information, and improves the expressive power and the distinguishing degree of the feature map.
5. The method for detecting the surface defects of the PCB based on the improved YOLOv8 algorithm according to claim 1, wherein in the third step, when a PCB surface defect detection model based on the improved YOLOv8 is trained, a normalized Wasserstein distance is introduced as a measurement standard, and a loss function is optimized;
L NWD ＝1-NWD(N a ,N b )
wherein L is NWD Is normalized Wasserstein loss, N a Is the Gaussian distribution of the predicted bounding box A, N b Is a gaussian distribution of a true bounding box B, NWD (N a ,N b ) Is the normalized Wasserstein distance of the predicted bounding box A and the actual bounding box B.
CN202311378097.5A 2023-10-24 2023-10-24 PCB surface defect detection method based on improved YOLOv8 algorithm Pending CN117422681A (en)

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