Article(id=1254362830812156466, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, articleNumber=null, orderNo=null, doi=10.13788/j.cnki.cbgc.2026.03.16, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749484800000, receivedDateStr=2025-06-10, revisedDate=1758988800000, revisedDateStr=2025-09-28, acceptedDate=null, acceptedDateStr=null, onlineDate=1776993003799, onlineDateStr=2026-04-24, pubDate=1774368000000, pubDateStr=2026-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776993003799, onlineIssueDateStr=2026-04-24, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776993003799, creator=13701087609, updateTime=1776993003799, updator=13701087609, issue=Issue{id=1254362823425974931, tenantId=1146029695717560320, journalId=1254119036117037056, year='2026', volume='48', issue='3', pageStart='1', pageEnd='190', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776993002036, creator=13701087609, updateTime=1776993258606, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1254363899764077151, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1254363899764077152, tenantId=1146029695717560320, journalId=1254119036117037056, issueId=1254362823425974931, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=142, endPage=151, ext={EN=ArticleExt(id=1254362834587030083, articleId=1254362830812156466, tenantId=1146029695717560320, journalId=1254119036117037056, language=EN, title=Intelligent Recognition and Measurement of Point Cloud Objects for Typical Ring-Ribbed Shell Structures, columnId=1254362827888722754, journalTitle=Ship Engineering, columnName=Ship Materials, Manufacturing Processes and Management, runingTitle=null, highlight=null, articleAbstract=
[Purpose] To overcome the limitations of the traditional random sample consensus (RANSAC) algorithm in cylindrical segmentation, a novel method is developed for segmenting point clouds of ring-ribbed shells by integrating structural features and statistical methods.
[Method] Initially, the model surface area feature is utilized to estimate the proportion of inliers, thereby enhancing the accuracy of initial parameters. Subsequently, principal component and radius constraints are introduced to enhance the accuracy of cylinder identification and reduce the number of iterations. Then, a weight function-based correction method is applied to mitigate outlier interference, thereby improving the accuracy of cylinder fitting. Finally, the DBSCAN algorithm clustered the point clouds of ring-ribs, and an improved RANSAC algorithm identified localized features, thus achieving precise measurement of component dimensions.
[Result] Experimental results show that the proposed method effectively addresses the intelligent recognition and dimensions measurement of components in various parts of the ring-ribs, significantly improving the recognition speed and accuracy of cylindrical shell and ring-ribs. The precision, recall, and overall accuracy of cylindrical shell reach 96.9%, 99.5% and 96.4% respectively, with a computational speed increase of approximately 4.6 times. The measurement error for ring-rib component dimensions is within 0.2%.
[Conclusion] Compared with traditional methods, the proposed method offers significant advantages in the accuracy and computational efficiency of point cloud segmentation.
, correspAuthors=Zhong WANG, 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, authorCompany=null, fund=null, authors=null, authorsList=Yuchao HAN, Fei PENG, Zhong WANG), CN=ArticleExt(id=1254362838047330905, articleId=1254362830812156466, tenantId=1146029695717560320, journalId=1254119036117037056, language=CN, title=典型环肋壳圈结构点云对象智能识别与测量, columnId=1254362829709050707, journalTitle=船舶工程, columnName=船舶材料、制造工艺与管理, runingTitle=null, highlight=null, articleAbstract=
［目的］ 针对传统随机采样一致性（RANSAC）算法在圆柱分割中的不足，结合结构特征和统计方法，提出一种环肋壳圈点云的分割方法。
［方法］ 利用模型表面积特征估计局内点比例，提高初始参数的精确度；加入主方向和半径限制，提高圆柱识别的准确性并减少迭代次数；使用基于加权函数的修正方法抑制局外点干扰，使圆柱拟合更逼近真值；通过DBSCAN算法对环肋点云聚类，并利用改进的RANSAC算法识别环肋构件的局部特征，实现构件尺寸的精确测量。
［结果］ 试验结果表明：所提方法有效解决环肋壳圈各部分构件的智能识别与尺寸测量问题，显著提高柱段及环肋的识别速度和精度。柱段分割的精确率、召回率和整体精度分别达到96.9%、99.5%和96.4%，计算速度提高约4.6倍；环肋构件尺寸测量误差在0.2%以内。
［结论］ 所提方法与传统方法相比，其点云的分割精确度和计算效率均具有显著优势。
, correspAuthors=王中, authorNote=null, correspAuthorsNote=
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本刊已许可中国学术期刊（光盘版）电子杂志社在中国知网及其系列数据库产品中以数字化方式复制、汇编、发行、信息网络传播本刊全文，本刊著作权使用费与本刊稿酬一并支付；本刊所刊登文章均无知识产权争议，不涉及泄露国家秘密、技术秘密或商业秘密。作者向本刊提交文章发表的行为即视为同意我编辑部上述声明，文责自负。, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=tjiY9cxZR4fXEKWQ5q/E2g==, magXml=+j9QonWvWNiSLz/stlw+pA==, pdfUrl=null, pdf=B0VMi80bAdvDS7+d7wQqdw==, pdfFileSize=1548744, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=yy7iReYPmFxsB+t9A8CZRg==, mapNumber=null, authorCompany=null, fund=null, authors=
韩玉超（1989—），男，博士研究生。研究方向：船舶智能制造、船舶数字化建造技术。
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韩玉超（1989—），男，博士研究生。研究方向：船舶智能制造、船舶数字化建造技术。
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