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To improve the accuracy and real-time performance of identifying unsafe behaviors of miners in the mine belt transportation area, and to address the problems of poor real-time performance and high false detection rate in existing manual monitoring methods, a dual-stream spatiotemporal fusion network (DS-SFNet) that integrated image features and human skeleton features was proposed. First, challenges such as low illumination and dust interference in underground environments were addressed by designing a sub-pixel convolutional block attention module (SPCBAM), which combined with sub-pixel convolution and depth wise separable convolution to optimize feature representation. Second, to mitigate the high computational resource consumption of the OpenPose model, its backbone feature extraction network was reconstructed using MobileNet v3 by incorporating dilated convolutions and cross-layer connections. Finally, a hierarchical feature fusion module was constructed to deeply integrate image features and skeletal trajectory features through spatiotemporal alignment and complementary modeling. The results demonstrate a recognition accuracy of 76.4% on HMDB51 (Human Motion Database 51) and 97.9% on UCF101 (University of Central Florida 101), outperforming the SlowFast model by 1.5% and 1.1%, respectively. On a self-built coal mine dataset containing four unsafe behaviors (climbing, crossing, leaning, and hand-leaning), the average recognition accuracy reaches 92.3%. The MobileNet v3-reconstructed OpenPose model reduces parameters to 11.5% of the original Visual Geometry Group 19 (VGG19) network while increasing inference speed by over 3 times. The complete framework achieves a single-frame processing time of 38.7 ms and a parameter count of 57.3 M.

, authors=Qinxia HAO, Jiaqian ZHANG^**, authorsList=Qinxia HAO, Jiaqian ZHANG, authorCompany=null, correspAuthors=Jiaqian ZHANG, 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=1282034759731745051, articleId=1282034759052267799, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=矿井输送带运输区矿工不安全行为识别模型, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为提升矿井输送带运输区矿工不安全行为识别的准确性与实时性，解决现有基于人工监控手段实时性差、误检率高的问题，提出一种融合图像特征与人体骨骼特征的双流时空融合网络（DS-SFNet）。首先，针对井下低光照、粉尘干扰环境，设计亚像素卷积块注意力模块（SPCBAM），通过亚像素卷积与深度可分离卷积优化特征表达；其次，针对OpenPose模型计算资源消耗大的问题，采用MobileNet v3网络重构其主干特征提取网络，并引入空洞卷积与跨层连接；最后，构建融入注意力机制的分层特征融合模块，通过时空对齐与互补性建模深度融合图像特征与骨骼轨迹特征。结果表明：DS-SFNet模型在51种人类动作数据库（HMDB51）和佛罗里达大学101类视频数据集（UCF101）上的识别准确率分别为76.4%和97.9%，较SlowFast模型分别提升1.5%和1.1%；在包含攀爬、跨越、倚靠、手搭4类行为的自建煤矿数据集中，平均识别准确率达92.3%；MobileNet v3重构的OpenPose模型参数量仅为原始视觉几何组网络（VGG19）的11.5%，推理速度提升3倍以上；模型单帧处理时间为38.7 ms，参数量为57.3 M。

, authors=郝秦霞副教授, 张家千^**, authorsList=郝秦霞副教授, 张家千, authorCompany=null, correspAuthors=张家千, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=slv2p4whq4pfGPGeiIpEVw==, magXml=GAeEKbF1/R+hBDM2Q6dUng==, pdfUrl=null, pdf=HdYJZDVRk39D1PQ0gTGB7g==, pdfFileSize=8322652, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=OEzilBNiVOvfnZerGX+DEw==, mapNumber=null, fund=null)}, authors=[Author(id=1282034761640153376, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034759052267799, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=haoqinxia@xust.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282034761749205282, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034759052267799, authorId=1282034761640153376, language=EN, stringName=Qinxia HAO, firstName=Qinxia, middleName=null, lastName=HAO, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an Shaanxi 710054, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282034761824702755, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282034759052267799, authorId=1282034761640153376, language=CN, stringName=郝秦霞副教授, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=西安科技大学 通信与信息工程学院，陕西 西安 710054, bio={"content":"

郝秦霞（1980—），女，陕西西安人，博士，副教授，主要从事物联网应用、矿山安全方面的研究。E-mail: haoqinxia@xust.edu.cn。

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郝秦霞（1980—），女，陕西西安人，博士，副教授，主要从事物联网应用、矿山安全方面的研究。E-mail: haoqinxia@xust.edu.cn。

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