深度学习的工人多种不安全行为识别方法综述

doi:10.3778/j.issn.1002-8331.2307-0168

摘要/Abstract

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。

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

Abstract: 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 various 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 summarized, 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.

Key words: deep learning, unsafe worker behavior, target detection, behavior recognition, construction site

苏晨阳, 武文红, 牛恒茂, 石宝, 郝旭, 王嘉敏, 高勒, 汪维泰. 深度学习的工人多种不安全行为识别方法综述[J]. 计算机工程与应用, 2024, 60(5): 30-46.

SU Chenyang, WU Wenhong, NIU Hengmao, SHI Bao, HAO Xu, WANG Jiamin, GAO Le, WANG Weitai. Review of Deep Learning Approaches for Recognizing Multiple Unsafe Behaviors in Workers[J]. Computer Engineering and Applications, 2024, 60(5): 30-46.

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