红外技术2024，Vol.46Issue(6)：654-662,9.
基于PA-YOLO v5的印制电路板缺陷检测
PCB Defect Detection Based on PA-YOLO v5
摘要
Abstract
The bare complex layout of PCBs cause low contrast,uneven brightness,small defect positions,and irregular shapes in detected images,resulting in a large number of parameters,overfitting,and loss of feature information with increasing network depth.In this study,a PCB detection model PA-YOLO v5 based on YOLO v5 and mixed attention mechanism fusion with higher accuracy is proposed to suppress interference from general features and ensure that the network pays more attention to the detailed features of defect targets during feature extraction.The adaptive bidirectional feature pyramid network(BiFPN)is taken as reference to fully utilize the different scales of each feature map,thereby assigning different weights to different detection targets,to improve the network's ability to express various features.Finally,the FReLU activation function is used to expand the ReLU space into a 2D activation function,which enhances the receptive field's ability to capture details and improves model robustness and generalization.Six types of defects were tested using the DeepPCB dataset,and the experimental results showed that the proposed PA-YOLO v5 detection model achieved an accuracy of 99.4%.The effectiveness of the model was verified through ablation and comparative experiments.关键词
缺陷检测/注意力机制/印刷电路板/深度学习Key words
defect detection/attention mechanism/printed circuit board/deep learning分类
计算机与自动化引用本文复制引用
陈锦妮,拜晓桦,李云红,田谷丰..基于PA-YOLO v5的印制电路板缺陷检测[J].红外技术,2024,46(6):654-662,9.基金项目
陕西省科技计划项目(2022GY-053). （2022GY-053）
