关键词: 印制电路板, 微小缺陷, YOLOv5, 注意力机制, 特征融合

Abstract: Defect detection is an important part of quality control in the production of Printed Circuit Board (PCB).Due to the tiny size of PCB surface defects and the complex of traverse layout,the existing detection algorithms cannot make full use of the characteristics of small defects,and its detection accuracy cannot meet the production requirements.To solve these problems,a You Only Look Once Version 5—Tiny Defect Detection (YOLOv5-TDD) algorithm for PCB minimal defect detection was presented.Based on YOLOv5,the shallow feature fusion branch in the neck network was added to improve the information flow efficiency of tiny defect features.The Squeeze and Excitation—SiLU (SE-SiLU) attention mechanism module was introduced to improve the network's attention to the tiny defect information of shallow features by assigning weights to the feature information.The experimental results showed that YOLOv5-TDD had 99.12% mAP  in PCB_DATASET defect dataset test,3.54% higher than YOLOv5,and better detection accuracy than other algorithms.

Key words: printed circuit board, tiny defect, YOLOv5, attention mechanism, feature fusion