Article(id=1308453991432614118, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453947069452650, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.01.020, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739808000000, receivedDateStr=2025-02-18, revisedDate=1762358400000, revisedDateStr=2025-11-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889341457, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889341457, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889341457, creator=13701087609, updateTime=1789889341457, 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=158, endPage=165, ext={EN=ArticleExt(id=1308453991688466663, articleId=1308453991432614118, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Intelligent Inspection of Evacuation Platform Based on Multi-technology Fusion, columnId=1308453984683971227, journalTitle=Urban Rapid Rail Transit, columnName=Intelligent Operation and Maintenance, runingTitle=null, highlight=null, articleAbstract=

Bolts on rail transit evacuation platforms are often concealed, making comprehensive manual inspection difficult and increasing the risk of loosening or detachment that could compromise operational safety. This paper proposes an intelligent inspection system integrating machine vision and laser scanning to achieve precise detection of bolt defects and clearance-intrusion trends. The system employs a dual-track autonomous robot equipped with a high-resolution camera and LiDAR, enabling comprehensive inspection through image recognition and point-cloud processing. First, high-resolution image acquisition combined with Adaptive Contrast Enhancement (ACE) is used to improve image quality. Optimized YOLOv11 and Mask R-CNN models are then applied for small-object detection and segmentation, enabling automatic identification of missing bolts, loose nuts, and insufficient bolt protrusion, with an accuracy of 94.4%. Furthermore, laser point-cloud modeling combined with a vector cross-product algorithm is employed to provide clearance-intrusion warnings with a precision of 2 cm. Field tests conducted on the Hangzhou-Haining Intercity Railway, across both elevated and tunnel sections, demonstrate an F1-score of 92.6% for bolt defect recognition and a 0% false alarm rate for clearance detection. The proposed system significantly enhances inspection efficiency and provides technical support for intelligent maintenance of rail transit infrastructure.

, authors=Ru WANG, Duanyang CAI, Huafeng ZHUGE, Cong WU, authorsList=Ru WANG, Duanyang CAI, Huafeng ZHUGE, Cong WU, 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=1308453992032399592, articleId=1308453991432614118, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于多技术融合的疏散平台智能巡检研究, columnId=1308453986550436510, journalTitle=都市快轨交通, columnName=智能运维, runingTitle=null, highlight=null, articleAbstract=

轨道交通疏散平台的螺栓因隐蔽性强而难以通过人工巡检全面检测，易出现松动或脱落隐患，影响运行安全，提出一种融合机器视觉与激光扫描的智能巡检系统，旨在实现螺栓缺陷与侵限趋势的精准检测。采用双轨式自走行机器人作为载体，集成高清相机与激光雷达，通过图像识别与点云处理技术完成全方位检测。首先利用高清图像采集结合自适应灰度增强算法（ACE）提升图像质量，采用改进的YOLO11和Mask R-CNN进行小目标检测与分割，实现对螺栓缺失、螺帽松动与露头不足的自动识别，准确率达94.4%；其次，通过激光点云建模与向量叉积算法，实现2 cm精度的侵限趋势预警。现场测试表明，该系统在杭海城际铁路的高架与隧道场景下，螺栓缺陷识别F1-score达92.6%，侵限检测误报率为0%，显著提升巡检效率，为轨道交通设施智能运维提供技术支撑。

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王儒，男，硕士，高级工程师，主要从事轨道交通土建结构设备智能维保，396474943@qq.com

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