Article(id=1308453928211870413, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.02.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756051200000, receivedDateStr=2025-08-25, revisedDate=1762790400000, revisedDateStr=2025-11-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889326384, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889326384, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889326384, creator=13701087609, updateTime=1789889326384, updator=13701087609, issue=Issue{id=1308453915104665783, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='2', pageStart='1', pageEnd='160', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889323259, creator='13701087609', updateTime=1789889766020, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455772237615687, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455772237615688, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=73, endPage=80, ext={EN=ArticleExt(id=1308453928442557134, articleId=1308453928211870413, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Research on Tunnel Core Soil Monitoring Technology Using YOLO-Based Visual Recognition, columnId=1152669336394183038, journalTitle=Urban Rapid Rail Transit, columnName=Civil Engineering Technology, runingTitle=null, highlight=null, articleAbstract=

In drill-and-blast tunnel construction, the geometric configuration and retained dimensions of the core soil directly influence tunnel settlement magnitude and surrounding rock stability. Traditional manual monitoring methods are inherently subjective, inefficient, and fail to meet real-time monitoring requirements. This paper proposes an automated core soil monitoring method based on an improved YOLO framework. By defining core soil feature points, a standardized annotation system is established. A lightweight detection model incorporating the Convolutional Block Attention Module (CBAM) is designed, effectively suppressing false detections caused by uneven illumination, dust interference, and blurred soil boundaries. Integrated with perspective geometry vanishing point prediction techniques, the VPTR model is employed for vanishing point detection and geometric constraints. This approach corrects perspective distortion errors in core soil cross-section shape calculation, incorporates core soil dimensions and proportions into potential risk classification criteria. For engineering deployment, knowledge distillation and dynamic precision-switching techniques are adopted to enable lightweight operation on edge computing devices, supporting the real-time decision-making demands of "monitoring while excavating".

, authors=Ruijun LIAN^{1, 2, 3}, Hanqing LI^{1, 2, 3}, Peiyin LYU^{3, 4, 5}, Songling XING⁶, authorsList=Ruijun LIAN, Hanqing LI, Peiyin LYU, Songling XING, 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=1308453928832627407, articleId=1308453928211870413, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=基于YOLO视觉识别的隧道核心土监测技术研究, columnId=1152669336603898239, journalTitle=都市快轨交通, columnName=土建技术, runingTitle=null, highlight=null, articleAbstract=

在矿山法隧道施工中，传统的人工监测方法存在主观性强、效率低且无法满足实时监测要求，针对此问题，提出一种基于改进YOLO模型框架的核心土自动监测方法，通过定义核心土特征点构建标准化标注体系，设计融合卷积块注意力模块的轻量化检测模型，有效抑制光照不均、粉尘干扰及土体边界模糊导致的误检。结合透视几何灭点预测技术，采用VPTR模型进行灭点检测与几何约束，修正核心土截面形态计算过程中因透视产生的偏差，并将核心土尺寸、比例纳入隐患分级标准。工程化部署中，采用知识蒸馏与动态精度切换技术实现边缘计算设备轻量化运行，支持“边掘进、边监测”的实时决策需求。

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廉瑞军，男，硕士，高级工程师，从事轨道交通建设过程管理和安全质量控制新型理论与技术的研究，Lianruijun@126.com

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廉瑞军，男，硕士，高级工程师，从事轨道交通建设过程管理和安全质量控制新型理论与技术的研究，Lianruijun@126.com

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