Article(id=1308453984591704260, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.01.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744819200000, receivedDateStr=2025-04-17, revisedDate=1756656000000, revisedDateStr=2025-09-01, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889339825, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889339825, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889339825, creator=13701087609, updateTime=1789889339825, updator=13701087609, issue=Issue{id=1308453947069452650, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='1', pageStart='1', pageEnd='165', issueExtLink='null', onlineDate='null', pubDate='1770652800000', pubDateStr='2026-02-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889330879, creator='13701087609', updateTime=1789889788596, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455866915639450, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455866915639451, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=143, endPage=149, ext={EN=ArticleExt(id=1308453986865017029, articleId=1308453984591704260, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Development and Application of an Intelligent Inspection System for Subway Protection Areas Based on Video Surveillance, columnId=1308453984683971227, journalTitle=Urban Rapid Rail Transit, columnName=Intelligent Operation and Maintenance, runingTitle=null, highlight=null, articleAbstract=

To address the issues of poor real-time performance and low accuracy in existing subway protection area supervision models, an intelligent inspection system was developed that integrates video surveillance with image recognition and edge computing technologies. Firstly, an edge computing system for protection area inspection was constructed using edge computing terminals and surveillance cameras, effectively reducing reliance on cloud servers. Secondly, the YOLOv8l algorithm was selected as the image recognition model for the intelligent inspection system and deployed on the edge computing terminals. Finally, a B/S architecture -based inspection cloud platform was developed for managing inspection equipment, emergencies, and operations. Through training and testing with approximately 100,000 images of 6 categories of mechanical risk sources under various scenarios, the model achieves a consistent recognition rate exceeding 90% in both daytime and nighttime conditions. Field tests conducted along Guangzhou Metro Line 5 demonstrate an overall system recognition rate of 85.42%, indicating the substantial potential of this video-based intelligent inspection system. This advancement holds significant practical implications for ensuring the sustainable development of urban rail transit systems.

, authors=Wenfeng BAI, Haitao LUO, Jing LIU, authorsList=Wenfeng BAI, Haitao LUO, Jing LIU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308453987154424006, articleId=1308453984591704260, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于视频监控的地铁保护区智能巡检系统研发与应用, columnId=1308453986550436510, journalTitle=都市快轨交通, columnName=智能运维, runingTitle=null, highlight=null, articleAbstract=

针对现有的地铁保护区监管模式实时性差、精准性不足等问题，将视频监控与图像识别、边缘计算等结合研发地铁保护区智能巡检系统。首先由边缘计算终端和监控摄像头构建地铁保护区巡检边缘计算系统，降低对云服务器的依赖；其次选择YOLOv8l作为智能巡检系统的图像识别算法并部署于边缘计算终端；最后基于B/S模式研发巡检云平台，进行巡检设备管理、应急管理以及运营管理。利用所采集的不同情况下的约10万张的6类风险源机械数据集，通过训练和测试使模型能够在白天和夜晚的识别率都保持在90%以上。在广州地铁5号线沿线实际测试结果显示，系统总体识别率达到85.42%，表明地铁保护区视频智能巡检系统具有巨大潜力，对于保障城市轨道交通的可持续发展具有重要现实意义。

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柏文锋，男，硕士，正高级工程师，从事城市轨道交通保护与监测方面研究，baiwenfeng_1983@126.com

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柏文锋，男，硕士，正高级工程师，从事城市轨道交通保护与监测方面研究，baiwenfeng_1983@126.com

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