DSASPP: Depthwise Separable Atrous Spatial Pyramid Pooling for PCB Surface Defect Detection

Abstract

Printed circuit board (PCB) defect detection is an important and indispensable part of industrial production. PCB defects, due to the small target and similarity between classes, in the actual production of the detection process are prone to omission and false detection problems. Traditional machine-learning-based detection methods are limited by the actual needs of industrial defect detection and do not show good results. Aiming at the problems related to PCB defect detection, we propose a PCB defect detection algorithm based on DSASPP-YOLOv5 and conduct related experiments on the PKU-Market-PCB dataset. DSASPP-YOLOv5 is an improved single-stage detection model, and we first used the K-means++ algorithm for the PKU-Market-PCB dataset to recluster the model so that the model is more in line with the characteristics of PCB small target defects. Second, we design the Depthwise Separable Atrous Spatial Pyramid Pooling (DSASPP) module, which effectively improves the correlation between local and global information by constructing atrous convolution branches with different dilated rates and a global average pooling branch. The experimental results show that our model achieves satisfactory results in both the mean average precision and detection speed metrics compared to existing models, proving the effectiveness of the proposed method.