Circuit board defect detection method based on improved YOLOv10 algorithm

This study focuses on improving the YOLOv10 algorithm to achieve more efficient PCB defect detection. We have innovatively replaced the original PSA layer with a PSCSA mechanism to more effectively capture and emphasize key feature information at different scales in the image, thereby improving the detection ability of subtle defects. Through this improvement, the model not only strengthens the learning ability of defect features under complex background, but also improves the accuracy and robustness of detection. The experimental results show that the optimized model shows significant performance improvement in the task of PCB defect identification. In summary, the PCB defect detection method proposed in this study based on improved YOLOv10 effectively improves the detection performance of the model through the introduction of PSCSA mechanism, provides strong technical support for the quality control of the electronics manufacturing industry, and demonstrates the great potential of deep learning technology in industrial applications.