Efficient and Precise Detection of Surface Defects on PCBs: A YOLO Based Approach.

Printed Circuit Board (PCB) is a significant component of the power system, and their surface defects may hinder electrical performance. Therefore, developing an efficient and precise PCB surface defect detection method is crucial for ensuring the state of the entire power system. In recent years, there has been growing interest in lightweight attention mechanisms that aim to achieve high accuracy with minimal computational cost.In this work, a single-stage object detection network based on YOLO v5m is proposed, which incorporates and compare 3 attention mechanisms to enhance the detection capabilities of the model, namely Coordinate Attention (CA), Convolutional Block Attention Module (CBAM), and Squeeze-and-Excitation (SE), In addition, the evaluation indicator Wise IoU (WIoU) has also been used to replace traditional IoU. Experimental results indicate that the proposed approach achieves mean Average Precision (mAP) of 97.8% and a frame rate of 80.1.Surpassing the performance of other compared models. The proposed approach has the potential to be deployed on edge device in the future.