深度学习的工人多种不安全行为识别方法综述OA北大核心CSTPCD

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.