现代雷达2026，Vol.48Issue(3)：117-124,8.DOI:10.16592/j.cnki.1004-7859.2025117
YOLOv8s-EWD:一种雷达网线接线缺陷检测模型
YOLOv8s-EWD:A Model for Ethernet Cable Wiring Defect Detection for Radar
摘要
Abstract
With the increasing demand for intelligent fault diagnosis in radar systems and the rapid development of machine learning technology,machine learning methods have been widely applied in the field of rapid fault localization for internal components of ra-dar.Addressing the current issues in radar systems where line-type faults cannot be directly fed back through the system,requiring manual troubleshooting which leads to inefficiency,as well as the difficulties in detecting small-sized and complexly wired network ca-ble defects in diverse environments,a radar ethernet cable defect detection model—YOLOv8s-EWD—is proposed.First,the HA_C2f module in the proposed model enhances the ability to express local features.Then,during the downsampling process of the head network,a depthwise convolution module combined with the C2f module is used to reduce the number of parameters in the head net-work,effectively reducing the model's parameter count while ensuring detection accuracy.Finally,a new P2 detection layer is added to strengthen the ability to capture fine-grained features.Experimental results show that the YOLOv8s-EWD model achieves signifi-cant improvements in fine-grained feature recognition,local feature description,model light-weighting,and detection accuracy.关键词
YOLOv8s模型/HA_C2f模块/深度卷积/缺陷检测/雷达网线Key words
YOLOv8s model/HA_C2f module/depthwise convolution(DWConv)/defect detection/ethernet cable for radar分类
信息技术与安全科学引用本文复制引用
李文锋,陆施楷,颜振亚,沙超..YOLOv8s-EWD:一种雷达网线接线缺陷检测模型[J].现代雷达,2026,48(3):117-124,8.基金项目
国家自然科学基金资助项目(62272244) （62272244）
金陵科技学院高层人才科研启动资助项目(jit-b-2021-09) （jit-b-2021-09）
2024年校级"科教融合"资助项目(2024KJRH16) （2024KJRH16）
2025年江苏省研究生实践创新计划(自然科学)资助项目(SJCX25_1300) （自然科学）
江苏省学位与研究生教育教学改革课题资助项目(YJSJG25_07) （YJSJG25_07）
