Deep Learning and Multi-scale Feature Fusion for PCB Surface Defect Detection
As the core of electronic equipment,the performance and reliability of printed circuit board(PCB)circuit board are crucial to electronic products.In view of the limitations of traditional inspection methods in terms of efficiency and accuracy,aiming to significantly improve the performance of PCB defect detection through technological innovation,the YOLOv8-Defect model is constructed,which is optimized on the basis of YOLOv8,including the introduction of SEAttens mechanism,Soft-NMS algorithm and Wise-IoU technology,and the C2f architecture is upgraded to the C3 architecture.Through advanced data augmentation techniques and model training strategies,YOLOv8-Defect has achieved a significant performance improvement in detecting PCB surface defects.Experimental results show that the model can not only efficiently identify small defects on the circuit board,but also realize real-time monitoring,ensuring the continuity and immediacy of the inspection process.The results not only bring innovative solutions to the field of industrial quality inspection,but also demonstrate the great application potential of deep learning technology in solving practical industrial challenges,and provide solid technical support for the improvement of electronic equipment quality and production efficiency.
data augmentationsurface defects on PCB circuit boardsmulti scale fusion of featuresYOLOv8 defectWise-IoUdeep learning
万方数据
维普
