基于YOLO的智能化铁路货车搭扣缺陷检测系统设计与实现

Design and Implementation of Intelligent Hasp Defect Detection System for Railway Freight Trains Based on YOLO

铁路货车列检管理是确保铁路行车安全和货物安全的重要环节,目前列检作业方式以人工检查或人机分工检查为主,检车员劳动强度较高,工作效率低,安全风险高。针对该问题,设计并实现基于YOLOv5模型的智能化铁路货车搭扣缺陷检测系统,通过采用缺陷检测与对比算法,实现搭扣座破、搭扣丢失以及搭扣未扣等缺陷智能检测、隐患自动报警,并在检车结束后自动生成检车报告,方便统计分析。实验结果表明:系统识别精度高,功能完善,可满足铁路货车列检作业的智能检测需求,从而为铁路货车列检作业向智能化发展提供有力的工具。

Railway freight train inspection management is an important part of ensuring railway operation safety and cargo security.Currently,the inspection process is mainly conducted manually or involves a combination of human and machine inspections.However,this approach results in high labor intensity for inspectors,low work efficiency,and increased safety risks.To address this issue,this paper proposed an intelligent defect detection system for railway freight trains based on the YOLOv5 model.The system used defect detection and comparative algorithms to detect defects such as broken or missing hasps,as well as uncoupled hasps automatically.It also provided automatic alarm notifications for potential hazards and generated inspection reports after the inspection was completed for convenient statistical analysis.Experimental results demonstrate that the system has high recognition accuracy and comprehensive functionality,and it can meet the intelligent inspection requirements of railway freight train inspections.The proposed system provides a powerful tool for advancing the intelligent development of railway freight train inspections.