Improved PCB Board Defect Detection Algorithm Based on YOLOv4
LI Zhijin
JIANG Kaiqiang
GAO Wei
LIU Zhongyang
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.
Keywords：defect detectionYOLOv4GhostNetGCT attentionblueprint separable convolution
Publication Date：2025-02-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 320-326 )