This study aims to analyze and summarize the development of personal protective equipment(PPE)detec-tion technology and its applications in construction site safety management,focusing on the role and effectiveness of sen-sors and deep learning in detection.A combination of literature review and data analysis was employed,with the Citespace system used to map recent advancements in PPE detection.By comparing sensor-based detection methods with deep learn-ing algorithms,including One-stage and Two-stage models,this paper comprehensively evaluates their practical effective-ness in PPE detection.The results indicate that deep learning techniques significantly enhance the accuracy and efficiency of PPE detection,particularly in complex environments and small-object detection.However,current detection technolo-gies still face challenges in terms of dataset diversity,equipment stability,and small-object detection.The conclusion high-lights the need for further optimization of multimodal detection technologies,along with the integration of emerging tech-nologies such as 5G and edge computing,to achieve more efficient and precise PPE detection and application.
personal protective equipmentdetectioncomputer visiondeep learningsensor
NETL
NSTL
万方数据
