Article(id=1281616662382219399, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.02.1372, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757433600000, receivedDateStr=2025-09-10, revisedDate=1763395200000, revisedDateStr=2025-11-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1783490823598, onlineDateStr=2026-07-08, pubDate=1772208000000, pubDateStr=2026-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783490823598, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783490823598, creator=13701087609, updateTime=1783490823598, updator=13701087609, issue=Issue{id=1281616655499366465, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='2', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1772208000000', pubDateStr='2026-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783490821957, creator='13701087609', updateTime=1783588693138, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027157069218127, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027157069218128, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=18, endPage=26, ext={EN=ArticleExt(id=1281616662579351688, articleId=1281616662382219399, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Method of coupling identifying unsafe behaviors of underground personnel based on dual-model algorithm, columnId=1277328386947150682, journalTitle=China Safety Science Journal, columnName=Safety Science Theories and Methods, runingTitle=null, highlight=null, articleAbstract=

In order to prevent safety accidents caused by unsafe behaviors of underground personnel and to ensure their safety, by utilizing advanced machine vision and computer technologies, the traditional YOLOv5s algorithm and OpenPose algorithm target detection models were improved, and a dual-model coupled algorithm for identifying unsafe behaviors of underground personnel was proposed. Through statistical analysis of the most common unsafe behaviors in current underground coal mines, the unsafe behaviors of miners were classified, including item-related, action-related, and area-related unsafe behaviors. According to the characteristics of miners' unsafe behaviors, the improved YOLOv5s algorithm and the OpenPose algorithm were coupled for recognition, and training and verification were conducted on public datasets and self-built datasets. The results show that compared with the current mainstream methods, the dual-model coupled recognition method has a significant improvement in recognition accuracy on self-built datasets and public datasets, with an increase of 5% to 10%, and can quickly and effectively identify unsafe behaviors of underground personnel.

, authors=Bo TAN¹, Longkun SUI^{**, 1}, Wei KE², Yan LIU², Quanjie ZHU³, Ning HE³, authorsList=Bo TAN, Longkun SUI, Wei KE, Yan LIU, Quanjie ZHU, Ning HE, authorCompany=null, correspAuthors=Longkun SUI, 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=1281616664445816971, articleId=1281616662382219399, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=双模型算法耦合识别井下人员不安全行为的方法, columnId=1277328387270112092, journalTitle=中国安全科学学报, columnName=安全科学理论与方法, runingTitle=null, highlight=null, articleAbstract=

为了防止由于井下人员的不安全行为造成的安全事故，并保障井下人员安全，利用先进的机器视觉和计算机技术，改进传统的YOLOv5s算法和OpenPose算法目标检测模型，提出一种双模型耦合识别井下人员不安全行为的算法；通过统计分析当前煤矿井下较为常见的不安全行为，将矿工不安全行为分为物品、动作、区域等3类；根据矿工的不安全行为特点，采用改进YOLOv5s算法和OpenPose算法耦合识别的方法，在公共数据集和自建数据集上进行训练和验证。结果表明：相比于当前主流的方法，双模型耦合识别方法在自建数据集和公共数据集的基础上识别准确率有较大提升（5%~10%），可对井下人员不安全行为进行快速、有效的识别。

, authors=谭波教授¹, 隋龙琨^{**, 1}, 柯巍², 刘衍², 朱权洁教授³, 何宁副教授³, authorsList=谭波教授, 隋龙琨, 柯巍, 刘衍, 朱权洁教授, 何宁副教授, authorCompany=null, correspAuthors=隋龙琨, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=d1Ovm+hSG8evKvP61wDbnQ==, magXml=1Fju0G3T/dY3Oj2dQJbigw==, pdfUrl=null, pdf=Zk/QWK6JCNni1WnWYjRyVQ==, pdfFileSize=10568511, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=xCsWnd47C89QNzueOs6JRA==, mapNumber=null, fund=null)}, authors=[Author(id=1281616664932356246, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616662382219399, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=tanbo709@126.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281616665024630936, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616662382219399, authorId=1281616664932356246, language=EN, stringName=Bo TAN, firstName=Bo, middleName=null, lastName=TAN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281616665121099929, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616662382219399, authorId=1281616664932356246, language=CN, stringName=谭波教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹中国矿业大学（北京） 应急管理与安全工程学院，北京 100083, bio={"content":"

谭波（1981—），男，湖南株洲人，博士，教授，主要从事煤矿火灾防治等方面的研究。E-mail:tanbo709@126.com。

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谭波（1981—），男，湖南株洲人，博士，教授，主要从事煤矿火灾防治等方面的研究。E-mail:tanbo709@126.com。

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