PCB Defect Detection Based on YOLOv5s

With the rapid development of the electronics manufacturing industry, the quality and reliability of printed circuit boards (PCBs) have become key factors in the performance of electronic products. Traditional PCB defect detection methods suffer from low efficiency and inaccurate results. This paper proposes a PCB defect detection method based on the YOLOv5s algorithm, aiming to improve the efficiency and accuracy of detection. YOLOv5s, as a lightweight real-time object detection model, is particularly suitable for automated detection tasks. This paper provides a detailed introduction to the basic structure and network design of the YOLOv5s model, including the Input, Backbone, Neck, and Head, and discusses its application in PCB defect detection. Through experimental design, we used an open-source PCB image dataset provided by the Peking University Intelligent Robotics Open Lab and trained and evaluated the model. The experimental results show that the model achieves high precision and recall rates in PCB defect detection, with a high mean Average Precision (mAP) value, demonstrating excellent performance. This study not only optimizes the quality control process of PCBs but also provides strong technical support for the automation and intelligent upgrading of the electronics manufacturing industry.