Article(id=1281627226303083088, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.01.1034, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757779200000, receivedDateStr=2025-09-14, revisedDate=1763654400000, revisedDateStr=2025-11-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1783493342232, onlineDateStr=2026-07-08, pubDate=1769529600000, pubDateStr=2026-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783493342232, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783493342232, creator=13701087609, updateTime=1783493342232, updator=13701087609, issue=Issue{id=1281627156769903305, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='1', pageStart='1', pageEnd='280', issueExtLink='null', onlineDate='null', pubDate='1769529600000', pubDateStr='2026-01-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783493325654, creator='13701087609', updateTime=1783588714348, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027246013624992, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027246013624993, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281627156769903305, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=267, endPage=274, ext={EN=ArticleExt(id=1281627226491826769, articleId=1281627226303083088, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Facepiece detection model based on feature fusion for personnel in tunnel operation scenarios, columnId=1149735805633081985, journalTitle=China Safety Science Journal, columnName=Occupational Health, runingTitle=null, highlight=null, articleAbstract=

In order to improve the efficiency of facepiece-wearing detection for tunnel operation workers, a feature fusion-based facepiece detection model was proposed. First, high quality query images were selected, and an image gallery was established. An image retrieval method was adopted to obtain samples and measure the similarity between query and gallery images, thereby iteratively expanding the dataset scale. Then, Histogram of Oriented Gradients (HOG) and Fisher features were extracted from the images. The Ant Lion Optimizer (ALO) was introduced to compute the optimal weight combination for the two types of features, which were subsequently fused. Finally, based on the fused features, a SVM was utilized to train a facepiece detection model, and experimental evaluations were conducted on the self-constructed dataset. The results indicate that the proposed model effectively accomplishes the task of facepiece-wearing detection in tunnel operation scenarios. Feature fusion enhances the image description and improves the detection accuracy of the model. Compared to using only HOG features or Fisher features, the accuracy is increased by 6% and 14%, respectively. The model meets the accuracy requirements for facepiece-wearing detection of workers in tunnel construction environments.

, authors=Binbin KE, Chenchen SUN^**, authorsList=Binbin KE, Chenchen SUN, authorCompany=null, correspAuthors=Chenchen SUN, 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=1281627226978366036, articleId=1281627226303083088, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=隧道作业场景下基于特征融合的人员面罩检测模型, columnId=1281608104106242978, journalTitle=中国安全科学学报, columnName=职业健康, runingTitle=null, highlight=null, articleAbstract=

为提高隧道作业场景下工人佩戴面罩的检测效率，提出基于特征融合的隧道作业人员面罩检测模型。首先，选取高质量查询图像并确定图像候选库，采用图像检索方法获取样本，度量查询图像与图像候选库的相似性，迭代扩充数据集；然后，从图像中提取方向梯度直方图（HOG）和Fisher特征，引入蚁狮优化算法（ALO），计算2种特征的最佳权重组合，并融合2种特征；最后，基于融合后的特征，利用支持向量机（SVM）训练面罩佩戴检测模型，在自建数据集上进行效果评估。结果表明：所提出的模型能够完成隧道作业场景下的面罩佩戴检测任务，特征融合能增强对图像的描述，提高模型的检测准确率，相比于单HOG特征和Fisher特征，准确率分别提升6%和14%，可满足隧道作业场景的工人面罩佩戴检测准确率要求。

, authors=柯彬彬, 孙辰晨副教授^**, authorsList=柯彬彬, 孙辰晨副教授, authorCompany=null, correspAuthors=孙辰晨副教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=0ioLjvK2+3Lrp7b5I/Sdzw==, magXml=6ScuHRiJhk3me6oRu01HYA==, pdfUrl=null, pdf=GeKPm7gjEcGDERJwNsbEmg==, pdfFileSize=9241011, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=mLSea08FY+l8IHxjtTKznw==, mapNumber=null, fund=null)}, authors=[Author(id=1281627227263578713, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627226303083088, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=kebin@email.cugb.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281627227343270491, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627226303083088, authorId=1281627227263578713, language=EN, stringName=Binbin KE, firstName=Binbin, middleName=null, lastName=KE, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281627227410379356, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281627226303083088, authorId=1281627227263578713, language=CN, stringName=柯彬彬, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=中国地质大学（北京） 工程技术学院，北京 100083, bio={"content":"

柯彬彬（2001—），男，江西九江人，硕士研究生，主要研究方向为呼吸防护装备。E-mail: kebin@email.cugb.edu.cn。

"}, bioImg=null, bioContent=

柯彬彬（2001—），男，江西九江人，硕士研究生，主要研究方向为呼吸防护装备。E-mail: kebin@email.cugb.edu.cn。

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