Research on PCB Defect Detection Based on Yolo Algorithm

With the rapid development of Industry 4.0 and intelligent manufacturing, product quality has become a key factor in the core competitiveness of enterprises. Especially in the field of electronics manufacturing, printed circuit board (PCB) as a core component, its quality directly affects the stability and reliability of the equipment. The traditional manual visual inspection method in PCB defect detection has limitations such as low efficiency and poor accuracy, which cannot meet the demand for efficient and high-precision detection in modern industrial production. In this paper, trap detection of PCB boards is based on the YOLO algorithm. The YOLOv9 algorithm achieves efficient target detection by integrating the Generalized Efficient Layer Aggregation Network (GELAN) and the Programmable Gradient Information (PGI), which improves the computational efficiency while retaining the key information. By comparing the detection effects of YOLOV9 and YOLOV8, it is shown that the YOLOv9 algorithm is able to achieve higher detection accuracy and realtime performance in PCB defect detection, which provides a reliable automated inspection solution for quality control in industrial production.