An improved YOLOv5 PCB defect detection

YiHang Zhao; HuiChen Yang; HaiHong Feng

doi:10.1117/12.2652341

2 November 2022 An improved YOLOv5 PCB defect detection

YiHang Zhao, HuiChen Yang, HaiHong Feng

Proceedings Volume 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022); 123511P (2022) https://doi.org/10.1117/12.2652341
Event: International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 2022, Nanjing, China

Abstract

Based on the low efficiency and high cost of conventional manual and electrical methods for detecting defects in PCB production, a PCB defect detection method based on YOLOv5 algorithm is proposed, which adds a prediction head for small object detection to form a four-dimensional detection, so as to improve the detection effect of small objects; ASFF (adaptive feature space fusion) is added to YOLOv5s original FPN + PANNET structure for feature fusion to ensure that each space can adaptively fuse different levels of feature information; GAM(global attention mechanism) is added to the original network, and attention operation is applied in all three dimensions , which strengthens the ability of model information extraction. The experimental results show that the improved defect detection method can accurately classify six kinds of defects, and the average accuracy can reach 98.8%. It has a certain reference value for the deep learning PCB defect detection method.

Citation Download Citation

YiHang Zhao, HuiChen Yang, and HaiHong Feng "An improved YOLOv5 PCB defect detection", Proc. SPIE 12351, International Conference on Advanced Sensing and Smart Manufacturing (ASSM 2022), 123511P (2 November 2022); https://doi.org/10.1117/12.2652341

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