目的 针对印制电路板(PCB)表面缺陷检测模型较大和速度慢的问题，提出一种基于YOLOv8的PCB轻量化表面缺陷检测框架EYOLOv8。方法 该框架以YOLOv8网络结构为基础，使用RevCol网络特征融合思想重构网络主干，引入Slim-neck设计思想重构颈部结构，使用卷积权重参数共享的机制重构检测头结构，在保持精度基本不变的同时，对整体网络结构进行了轻量优化设计，最终使用WIoU损失函数对轻量化模型训练过程进行优化。结果 在公共数据集上的实验结果表明，EYOLOv8较YOLOv8模型大小减少了46%，检测精度mAP50值达97.7%，检测速度达256帧/s，模型大小为3.3 MB。结论 相比其他算法，EYOLOv8在PCB表面缺陷检测设备上部署更有竞争力。

The work aims to propose a lightweight printed circuit board (PCB) surface defect detection framework, EYOLOv8, based on YOLOv8, to address the issues of large model size and slow speed in surface defect detection of PCB. Based on the YOLOv8 network structure, a network backbone was reconstructed according to the RevCol network feature fusion concept, the neck structure was redesigned according to the Slim-neck design concept, and the detection head structure was reconstructed according to the mechanism of convolutional weight parameter sharing. While maintaining nearly the same accuracy, the overall network structure was optimized for lightweight design. Finally, the training process of the lightweight model was optimized with the WIoU loss function. Experimental results on public datasets showed that EYOLOv8 reduced the model size by 46% compared with YOLOv8, achieved a detection accuracy mAP50 of 97.7%, and operated at a detection speed of 256 frames per second with a model size of 3.3 MB. Compared with other algorithms, EYOLOv8 demonstrates greater competitiveness when deployed on PCB surface defect detection devices.