面向印刷电路板缺陷检测的轻量化YOLOv8n-LSCNet目标检测模型OA

To address the problems of complex panel circuits,small defects,and difficulty in balancing detec-tion accuracy and efficiency in surface defect detection of printed circuit boards,a lightweight and efficient YOLOv8n-LSCNet object detection model is proposed.First,based on the YOLOv8n model,a C2f-OREPA module is introduced to enhance feature extraction capability utilizing online re-parameterization techniques.Second,a lightweight detection head is designed to reduce redundant computations through shared convolution operations.Finally,an extended intersection over union(EIoU)loss function is adopted to optimize bounding box regression accuracy.The model is trained and tested on the Peking University printed circuit board(PCB)dataset,and both ablation and comparative experiments are conducted to verify the effectiveness of each mod-ule.The results show that compared to the YOLOv8n model,the YOLOv8n-LSCNet model improves preision and mean average accuracy(intersection over union threshold≥0.50)by 0.94%and 0.47%,respectively,while reducing parameters and floating-point operations by 21.4%and 19.7%.The proposed model achieves a well-balanced trade-off between accuracy and efficiency,demonstrating strong potential for engineering appli-cations.