Article(id=1311351279188799604, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20250433, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741536000000, receivedDateStr=2025-03-10, revisedDate=1743523200000, revisedDateStr=2025-04-02, acceptedDate=1744646400000, acceptedDateStr=2025-04-15, onlineDate=1790580108686, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580108686, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580108686, creator=13701087609, updateTime=1790580108686, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=193, endPage=197, ext={EN=ArticleExt(id=1311351279335600245, articleId=1311351279188799604, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Research on Machine Vision Measurement Technology for Dam Surface Settlement Based on Natural Markers, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In response to the problem of traditional machine vision technology requiring specialized targets to assist in measuring dam surface settlement in complex terrain, this study proposes a machine vision measurement technology for dam surface settlement using the natural patterns or natural features (such as bedrock, mountain body, etc.) of the existing objects on the dam surface as markers. A fixed point region image registration method that simplifies the projection model has been developed. By deriving and simplifying the estimation of the projection model, the application scenarios of the model have been extended, and the image can be effectively corrected. An accelerated robust feature transformation algorithm is integrated to extract the features of natural markers, accurately match the image feature lattice, and then calculate the average pixel displacement of the markers. By obtaining the proportional relationship between pixel distance and physical distance, the true displacement of the markers can be calculated. The experimental results show that within a monitoring distance of 70 m, the measurement accuracy is in the range of -1.00 mm and 1.00 mm, effectively solving the problem of low-cost wide area monitoring of dam surface settlement, which can be applied in practical engineering.

, authors=Kun WANG¹, Deng-hua LI^{2, 3}, Yong DING¹, authorsList=Kun WANG, Deng-hua LI, Yong DING, authorCompany=null, correspAuthors=Deng-hua LI, 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=1311351279666950262, articleId=1311351279188799604, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=基于自然标识物的大坝表面沉降机器视觉测量技术研究, columnId=1207271181661589640, journalTitle=水电能源科学, columnName=水利枢纽、水利建筑物, runingTitle=null, highlight=null, articleAbstract=

针对传统机器视觉技术在复杂地形下需要专用靶标辅助测量大坝表面沉降的难题，利用坝体表面已有物体的天然纹路或自然地物（如基岩、山体等）作为标识物，提出了一种大坝表面沉降机器视觉测量技术，研发了简化投影模型的不动点区域图像配准方法，通过对投影模型推导并简化估计拓宽了模型的应用场景，并可有效修正图像，融合了加速稳健特征转换算法，提取自然标识物的特征，精准匹配图像特征点阵，进而计算标识物的平均像素位移，通过获取像素距离与物理距离的比例关系，计算得到标识物的真实位移。试验结果表明，在70 m监测距离内，测量精度≤±1.00 mm，有效解决了大坝表面沉降低成本广域监测的难题，可应用于实际工程中。

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王堃（2001-），男，硕士研究生，研究方向为结构健康检测，E-mail: njust2023@163.com

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王堃（2001-），男，硕士研究生，研究方向为结构健康检测，E-mail: njust2023@163.com

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