计算机与数字工程2025，Vol.53Issue(2)：320-326,7.DOI:10.3969/j.issn.1672-9722.2025.02.005
基于YOLOv4改进的PCB板缺陷检测算法
Improved PCB Board Defect Detection Algorithm Based on YOLOv4
摘要
Abstract
Aiming at the problems of low precision,slow inference speed and large model size in the current PCB defect detec-tion method in the industry,an improved PCB defect detection method based on YOLOv4 is proposed.Firstly,the YOLOv4 back-bone network is replaced with a GhostNet network,which greatly reduces the number of parameters of the backbone feature extrac-tion network and reduces the size of the model.Secondly,the GCT attention mechanism is added to the backbone network to en-hance feature extraction capabilities without increasing computational complexity,improve the accuracy,and finally use the blue-print convolution to reduce the computational complexity of the algorithm and improve the detection accuracy to achieve lightweight.Using the PCB defect data set published by the intelligent robot open laboratory of Peking university to conduct experiments,the ex-perimental results show that the proposed improved algorithm is lightweight and efficient.Compared with the original algorithm,the mAP accuracy and detection speed are improved,and the model size is reduced,it can solve the current problems.关键词
缺陷检测/YOLOv4/GhostNet/GCT注意力/蓝图卷积Key words
defect detection/YOLOv4/GhostNet/GCT attention/blueprint separable convolution分类
信息技术与安全科学引用本文复制引用
李致金,江凯强,高伟,刘忠洋..基于YOLOv4改进的PCB板缺陷检测算法[J].计算机与数字工程,2025,53(2):320-326,7.基金项目
国家自然科学基金项目(编号:61971167)资助. （编号:61971167）
