计算机工程与应用2024，Vol.60Issue(5)：30-46,17.DOI:10.3778/j.issn.1002-8331.2307-0168
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 in Workers
摘要
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 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Key words
deep learning/unsafe worker behavior/target detection/behavior recognition/construction site分类
信息技术与安全科学引用本文复制引用
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):30-46,17.基金项目
国家自然科学基金(62066035) （62066035）
内蒙古自治区高等学校科学技术研究项目(NJZY22374). （NJZY22374）
