Article(id=1282030064225194620, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.08.0176, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741968000000, receivedDateStr=2025-03-15, revisedDate=1747670400000, revisedDateStr=2025-05-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589386278, onlineDateStr=2026-07-09, pubDate=1756310400000, pubDateStr=2025-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589386278, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589386278, creator=13701087609, updateTime=1783589386278, updator=13701087609, issue=Issue{id=1282030021623657005, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='8', pageStart='1', pageEnd='276', issueExtLink='null', onlineDate='null', pubDate='1756310400000', pubDateStr='2025-08-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589376121, creator='13701087609', updateTime=1783589731222, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031511155228698, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031511155228699, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282030021623657005, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=40, endPage=47, ext={EN=ArticleExt(id=1282030064418132605, articleId=1282030064225194620, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Identification model of unsafe behaviors among operators in machining workshops, columnId=1149733271128420907, journalTitle=China Safety Science Journal, columnName=Safety social science and safety management, runingTitle=null, highlight=null, articleAbstract=

To enhance the safety management of operators in machining workshops, an identification model based on YOLOv11 was constructed. The YOLOv11 model was improved by integrating the MetaFormer architecture, Mixed Aggregation Network (MANet) module, and Adaptive Feature Grid Convolution Attention (AFGC Attention) mechanism. A video dataset captured in a real workshop environment was established to validate the identification model. The results show that the improved YOLOv11 model can identify three types of behaviors, namely unattended operation, operating without a face shield, and operating without protective clothing, with F₁scores exceeding 0.93 for all categories. The improved model demonstrates a significant enhancement in identifying small-sized targets, with the F₁ score for identifying glove-wearing behavior increasing from 0.684 to 0.708, and the mAP@0.5 value rising from 0.604 to 0.651. The research findings may provide technical support for the identification and early warning of unsafe behaviors among operators in machining workshops.

, authors=Xiaofeng HU, Teng TENG, Jinming HU, Jiajun WEN, authorsList=Xiaofeng HU, Teng TENG, Jinming HU, Jiajun WEN, 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=1282030064841757312, articleId=1282030064225194620, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=机械加工车间作业人员不安全行为识别模型, columnId=1149733271296193071, journalTitle=中国安全科学学报, columnName=安全社会科学与安全管理, runingTitle=null, highlight=null, articleAbstract=

为提高机械加工车间作业人员的安全管理水平，构建基于YOLOv11的识别模型，融合元架构视觉模块（MetaFormer）、混合聚合网络（MANet）以及自适应特征网格卷积注意力机制（AFGC Attention），改进YOLOv11模型，并建立真实车间环境下的视频数据集，验证上述模型。结果表明：改进YOLOv11模型能够识别无人值守、未佩戴面罩作业、未穿着防护服作业3种行为，F₁指数均达到0.93以上；改进的模型在小尺寸目标识别方面的性能具有显著提升，识别戴手套作业行为的F₁指数从0.684提升至0.708，平均精度均值（mAP）@0.5值从0.604提升到0.651。

, authors=胡啸峰副教授, 滕腾, 胡今鸣, 温家骏, authorsList=胡啸峰副教授, 滕腾, 胡今鸣, 温家骏, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=k7Szpr45lVf+pSI//YdeUQ==, magXml=zILDoGT/6yoZSXMJ0j/fMw==, pdfUrl=null, pdf=Cw2sMv3cs/0uYaEslBd+gg==, pdfFileSize=9453972, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=XoFNCQbKaiSyaaOn8gfMQg==, mapNumber=null, fund=null)}, authors=[Author(id=1282030066821468808, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030064225194620, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=huxiaofeng@ppsuc.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282030066901160587, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282030064225194620, authorId=1282030066821468808, language=EN, stringName=Xiaofeng HU, firstName=Xiaofeng, middleName=null, lastName=HU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=¹School of Information and Network Security, People's Public Security University of China, Beijing 100038, China
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胡啸峰（1986—），男，河北唐山人，工学博士，副教授，主要从事风险评估与预测预警技术研究。E-mail: huxiaofeng@ppsuc.edu.cn。

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胡啸峰（1986—），男，河北唐山人，工学博士，副教授，主要从事风险评估与预测预警技术研究。E-mail: huxiaofeng@ppsuc.edu.cn。

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