北京交通大学学报2025，Vol.49Issue(6)：55-63,9.DOI:10.11860/j.issn.1673-0291.20250115
基于改进YOLOv5的高铁隧道漏缆卡具检测方法
Detection method for leaky cable clamps in high-speed railway tunnels based on improved YOLOv5
摘要
Abstract
To address the low efficiency of manual inspection and the difficulty of processing massive data when detecting leaky cable clamps in high-speed railway tunnels,this study proposes a detection method based on an improved YOLOv5 model.First,a Ghost Spatial Convolutional Spatial Pyramid(GSCSP)module is designed,where Ghost convolution replaces standard convolution to reduce fea-ture redundancy and achieve model lightweighting.Second,a lightweight Efficient Channel Attention Network(ECANet)is integrated to strengthen the model's ability to distinguish clamp features from com-plex tunnel backgrounds and improve the detection accuracy of small objects.Then,a structured chan-nel pruning strategy is applied,in which redundant channels are pruned based on the scaling factors of Batch Normalization(BN)layers,achieving a lightweight architecture while maintaining model accu-racy.Finally,a dataset covering various states of leaky cable clamps under real high-speed railway op-eration scenarios is constructed.Data diversity is enhanced through methods such as Gaussian noise ad-dition and data stitching,providing a richer and more robust sample foundation for subsequent model training.Experimental results show that the improved model reduces the number of parameters by 72.1%while preserving detection accuracy and real-time performance.The findings provide a refer-ence for the intelligent operation and maintenance of railway communication equipment.关键词
计算机应用/卡具检测/通道注意力/模型剪枝Key words
computer application/clamp detection/channel attention/model pruning分类
信息技术与安全科学引用本文复制引用
张云佐,张璐琦,孙玉川,李滢旭,王宁..基于改进YOLOv5的高铁隧道漏缆卡具检测方法[J].北京交通大学学报,2025,49(6):55-63,9.基金项目
国家自然科学基金(61702347) （61702347）
驻冀高校重大科技专项(2512602307A) （2512602307A）
河北省自然科学基金(F2022210007) （F2022210007）
河北省教育厅科学技术(CXZX2025049) （CXZX2025049）
中央引导地方科技发展资金(226Z0501G) National Natural Science Foundation of China(61702347) （226Z0501G）
Major Science and Technology Project of Universities in Hebei Province(2512602307A) （2512602307A）
Natural Science Foundation of Hebei Province(F2022210007) （F2022210007）
Science and Technology Project of Hebei Education Department(CXZX2025049) （CXZX2025049）
Central Guiding Fund for Local Scientific and Technological Development(226Z0501G) （226Z0501G）
