Rapid Detection of PCB Defects Based on YOLOx-Plus and FPGA

Abstract

During the production process, Printed circuit boards (PCBs) may encounter many defect issues which can severely affect the functionality of the circuit. However, existing PCB defect detection methods suffer from low detection accuracy and slow detection speed. Considering the requirements of PCB factories for detection accuracy and real-time performance This paper first made structural improvements to the existing YOLOx defect detection algorithm, introducing PAN+FPN, SimAM, and SIoU modules to improve the detection accuracy of the algorithm, and named it YOLOx-Plus. Then, algorithm acceleration is achieved by quantifying network parameters and designing FPGA accelerators. In the experiment, the average detection accuracy of YOLOx-Plus is 93.2%, the network loss is reduced by 1.094, the model size is compressed by 64%, detection speed is improved by 68.1%, and the FPS reaches 72.6. The experimental results show that the proposed PCB defect detection method based on YOLOx-Plus and FPGA can efficiently detect typical defects in PCB boards, overcome the limitations of existing methods, and have a wide range of practical applications.