面向印刷电路板缺陷检测的轻量化 YOLOv8n-LSCNet目标检测模型

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中图分类号： TP391 文献标志码：A 文章编号： 1000-5013（2026）01-0061-07

Abstract：To addressthe problems of complex panel circuits，smalldefects，and difficultyin balancing detectionaccuracyand 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 efectiveness of each module.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 eficiency，demonstrating strong potential for enginering applications.

Keywords：printed circuit board （PCB） defect detection； lightweight detection; YOLOv8n; smalltarget detection；loss function

印刷电路板（PCB）是现代电子工业的核心组件之一，广泛应用于计算机、通信设备、消费电子、医疗设备、汽车电子及航空航天等领域。（剩余9974字）