Article(id=1321893315067367980, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760198400000, receivedDateStr=2025-10-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093525901, onlineDateStr=2026-10-27, pubDate=1781712000000, pubDateStr=2026-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093525901, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093525901, creator=13701087609, updateTime=1793093525901, updator=13701087609, issue=Issue{id=1321893272490987695, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='6', pageStart='1', pageEnd='312', issueExtLink='null', onlineDate='null', pubDate='1781712000000', pubDateStr='2026-06-18', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093515751, creator='13701087609', updateTime=1793094575775, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897718625194101, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897718625194102, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893272490987695, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=225, endPage=234, ext={EN=ArticleExt(id=1321893315289666093, articleId=1321893315067367980, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=A Recognition Method for Operational Personnel Behavior State in Fully Mechanized Mining Face Based on Multi-Scale Optimized YOLO, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the frequent accidents caused by uneven illumination, complex backgrounds, and human rule-breaking operations in underground coal mines, a personnel behavior recognition method based on the fusion and optimization of improved Retinex and YOLOv5 was proposed. Firstly, an improved multi-scale Retinex algorithm was employed to enhance underground images. Through multi-scale fusion and color restoration techniques, the interference of low illumination, coal dust, and water mist on the images was eliminated, thereby improving image clarity and color fidelity. Subsequently, the enhanced image data were input into YOLOv5 network to construct a target detection model based on the fusion and optimization of Retinex and YOLOv5, which was adapted to detection scenarios with dense equipment and complex backgrounds in underground mines. The experimental results show that the average accuracy of this method for recognizing helmet-wearing behavior and on-site operational violation postures of underground personnel reaches over 95% and 85%, respectively. This method can achieve the precise identification and judgment of rule-breaking behavior by underground personnel, providing technical support for intelligent safety supervision in coal mines.

, authors=Zhaowei Shi¹, Xusheng Xue², Hui Zhu¹, Enqiao Zhang², Yifeng Guo², Jianing Yue², authorsList=Zhaowei Shi, Xusheng Xue, Hui Zhu, Enqiao Zhang, Yifeng Guo, Jianing Yue, 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=1321893315604238894, articleId=1321893315067367980, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于多尺度优化YOLO的综采工作面作业人员行为状态识别方法, columnId=1226462295946085268, journalTitle=矿业研究与开发, columnName=矿山机电与矿业智能化, runingTitle=null, highlight=null, articleAbstract=

针对煤矿井下光照不均、背景复杂及人为违规操作导致的事故频发问题，提出一种基于改进Retinex与YOLOv5融合优化的井下人员行为识别方法。首先，采用改进多尺度Retinex算法对井下图像进行增强处理，通过多尺度融合与颜色恢复技术，消除低照度、煤尘、水雾对图像的干扰，提升图像清晰度与色彩还原度；其次，将增强后的图像数据输入YOLOv5网络，构建基于Retinex与YOLOv5融合优化的目标检测模型，以适配井下设备密集、背景复杂的检测场景。试验结果表明：该方法对井下人员安全帽佩戴行为和现场作业违规姿态识别的平均精确率分别达95%和85%以上，可实现井下人员违规行为的精确识别与判定，为煤矿安全智能化监管提供了技术支撑。

, authors=史兆伟¹, 薛旭升², 朱辉¹, 张恩桥², 郭逸风², 岳佳宁², authorsList=史兆伟, 薛旭升, 朱辉, 张恩桥, 郭逸风, 岳佳宁, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=N3A1Pd3wKffgc7U9084EQA==, magXml=E40mxG/lwnJJbog30nvY4w==, pdfUrl=null, pdf=2psyrgjly0nOisN4oxgrhQ==, pdfFileSize=3069017, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=nQjkOE9x/c1qn8lPMDPYQA==, mapNumber=null, fund=null)}, authors=[Author(id=1321893316078195254, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893315067367980, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=shizhaowei393@lzre.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893316162081336, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893315067367980, authorId=1321893316078195254, language=EN, stringName=Zhaowei Shi, firstName=Zhaowei, middleName=null, lastName=Shi, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Lanzhou Resources Environment Vocational and Technical University, Lanzhou, Gansu 730123, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893316237578809, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893315067367980, authorId=1321893316078195254, language=CN, stringName=史兆伟, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．兰州资源环境职业技术大学，甘肃 兰州　730123, bio={"content":"

史兆伟（1985—），男，陕西岐山人，博士，副教授，主要从事应急管理、安全生产的教学及管理工作。E-mail:shizhaowei393@lzre.edu.cn

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史兆伟（1985—），男，陕西岐山人，博士，副教授，主要从事应急管理、安全生产的教学及管理工作。E-mail:shizhaowei393@lzre.edu.cn

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