铁道运输与经济2024，Vol.46Issue(7)：97-105,9.DOI:10.16668/j.cnki.issn.1003-1421.2024.07.12
基于图像智能分析的在途列车作业视频智能化巡检方案研究
Intelligent Inspection Scheme for In-Transit Train Operation Video Based on Intelligent Image Analysis
摘要
Abstract
The implementation of operating standards by passenger train attendants is crucial for the safety of railway passenger transportation.To supervise the implementation of job standards by employees and to enhance the efficiency of video inspection and safety management,a job standard control and analysis system based on image intelligent analysis was proposed and constructed by integrating technologies such as target recognition and image intelligent analysis.This system was studied for the intelligent inspection of in-transit train operations videos.This system was utilized to analyze and determine whether the operation of train attendants complies with the operating standards.It intelligently identified and analyzed existing problems,and automatically summarized the identification results.In addition,it provided an important basis for work assessment,correcting violations,and conducting post-analysis.The results show that compared to manual retrieval and analysis methods,image intelligent analysis technology has the advantages of efficiency,accuracy,comprehensiveness,and uniformity.It effectively solves the problems of many missed inspections,inconsistent inspection standards,small coverage,and low efficiency in manual retrieval methods.It strengthens the implementation of standardized and regulated operations by the passenger train attendants and provides important assurance for improving the quality of passenger transport services and ensuring safe train operation.关键词
旅客运输安全/图像智能分析/作业标准管控/智能化巡检/标准化、规范化作业Key words
Passenger Transportation Safety/Intelligent Image Analysis/Operation Standard Management and Control/Intelligent Inspection/Standardized and Regulated Operations分类
交通工程引用本文复制引用
曲志恒,张琪,李伟,杨建新,张斌科..基于图像智能分析的在途列车作业视频智能化巡检方案研究[J].铁道运输与经济,2024,46(7):97-105,9.基金项目
中国铁路兰州局集团有限公司科技发展项目(LZJKY2021011-2) （LZJKY2021011-2）
