现代信息科技2025，Vol.9Issue(10)：34-38,5.DOI:10.19850/j.cnki.2096-4706.2025.10.007
基于改进YOLOv5算法的钢轨扣件状态检测
Rail Fastener Condition Detection Based on Improved YOLOv5 Algorithm
摘要
Abstract
Aiming at the problems of low detection efficiency,high cost,high manual work intensity and high risk coefficient in traditional rail fastener detection methods,this paper attempts to apply Deep Learning to rail fastener detection.This paper takes rail fasteners as the research object,and combines the CBAM attention mechanism and YOLOv5 model to detect rail fasteners.The experimental results show that the improved YOLOv5 rail fastener detection model can effectively detect rail defects.The detection accuracy is 0.93,the recall rate is 0.86,and the F1 score is 0.92,indicating that the model provides an effective detection method for rail fastener detection.关键词
钢轨扣件/缺陷分类/YOLOv5/CBAMKey words
rail fastener/defect classification/YOLOv5/CBAM分类
信息技术与安全科学引用本文复制引用
狄嘉钰,胡璐萍,臧凯,王剑楠,潘光权..基于改进YOLOv5算法的钢轨扣件状态检测[J].现代信息科技,2025,9(10):34-38,5.基金项目
大学生创新训练计划项目(2024DC07) （2024DC07）
陕西省教育厅科研计划项目(23JK0531) （23JK0531）
