深度学习的工人多种不安全行为识别方法综述  

Review of Deep Learning Approaches for Recognizing Multiple Unsafe Behaviors inWorkers

作　　者：苏晨阳 武文红[1] 牛恒茂[2] 石宝 郝旭 王嘉敏 高勒 汪维泰 SU Chenyang;WU Wenhong;NIU Hengmao;SHI Bao;HAO Xu;WANG Jiamin;GAO Le;WANG Weitai(College of Information Engineering,Inner Mongolia University of Technology,Hohhot 010080,China;College of Construction Engineering and Surveying and Mapping,Inner Mongolia Technical College of Construction,Hohhot 010080,China)

机构地区：[1]内蒙古工业大学信息工程学院,呼和浩特010080 [2]内蒙古建筑职业技术学院建筑工程测绘学院,呼和浩特010080

出　　处：《计算机工程与应用》2024年第5期30-46,共17页Computer Engineering and Applications

基　　金：国家自然科学基金(62066035);内蒙古自治区高等学校科学技术研究项目(NJZY22374)。

摘　　要：随着深度学习的发展,目标检测和行为识别方法在工人不安全行为识别领域取得了较大进展,对近年来国内外相关研究工作进行系统性归纳,详细阐述了目标检测方法和行为识别方法中的常用模型和效果,重点评述了两类方法在不安全行为识别上的应用和两类方法结合使用的相关研究,对各种方法的优势、局限性、识别行为类别及适用场景进行了全面分析对比。在此基础上,针对近年来目标检测和行为识别的优化措施,总结了常用的优化方向和手段,归纳了在不安全行为识别上成功应用的改进方法,梳理了该研究领域的难点和问题,并给出建议和未来发展趋势展望,为该领域的研究提供参考和借鉴。With the development of deep learning,target detection and behavior recognition methods have made great progress in the field of worker unsafe behavior recognition,this paper systematically summarizes the relevant research work at home and abroad in recent years,elaborates the commonly used models and effects of target detection methods and behavior recognition methods,focuses on reviewing the application of the two types of methods in the recognition of unsafe behaviors and the relevant research on the combination of the two types of methods,and provides a comprehensive analysis and comparison on the advantages,limitations,recognized behavior categories and applicable scenarios of vari-ous methods are comprehensively analyzed and compared.On this basis,the optimization measures for target detection and behavior recognition in recent years are summarized,the commonly used optimization directions and means are sum-marized,the improvement methods successfully applied in unsafe behavior recognition are summarized,the difficulties and problems in this research field are sorted out,and the suggestions and future development trends are given,which will provide references and lessons for the research in this field.

关 键 词：深度学习 工人不安全行为 目标检测 行为识别 施工现场 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]