农机使用与维修Issue(8)：1-6,6.DOI:10.14031/j.cnki.njwx.2026.08.001
基于YOLOv10深度学习的无人机接触网缺陷检测研究
Research on UAV-based catenary defect detection using YOLOv10 deep learning
摘要
Abstract
As the core power supply component of electrified railway systems,the catenary's operational state directly af-fects train operation safety.During UAV inspection for catenary component defects,the components are featured with numerous small targets,complex backgrounds and multi-scale characteristics.To address this problem,this paper pro-poses a UAV catenary defect detection scheme based on YOLOv10 deep learning.A dataset including four typical de-fects(insulator damage,conductor strand breakage,connector loosening and foreign matter hanging)is constructed.The CBAM attention mechanism is introduced into the YOLOv10 backbone to suppress background interference and en-hance small-target feature extraction,while the BiFPN is adopted in the neck network to optimize multi-scale feature fusion and reduce missed/false detections.Experimental results show that the mAP@0.5 of the improved model reaches 92.3%,which effectively improves the UAV inspection capability for catenary defects and provides reliable technical support for intelligent catenary detection.关键词
目标检测/YOLOv10/无人机(UAV)/接触网/CBAM/BiFPNKey words
object detection/YOLOv10/Unmanned Aerial Vehicle(UAV)/catenary/CBAM/BiFPN分类
交通工程引用本文复制引用
张亚..基于YOLOv10深度学习的无人机接触网缺陷检测研究[J].农机使用与维修,2026,(8):1-6,6.基金项目
三明市引导性科技项目计划(2025-G-059) （2025-G-059）
