Enhancing the Time Efficiency of Personal Protective Equipment (PPE) Detection in Real Implementations Using Edge Computing

Multiple studies have investigated the use of computer vision to enhance construction workers' safety by detecting personal protective equipment (PPE). However, implementing smart and automated PPE detection in near real time in real practices is still a significant challenge. The performance of PPE detection (e.g., accuracy) in near real-time implementations (i.e., time efficiency) has not been adequately studied to date. Thus, this study proposed an edge computing-based method for detecting PPE gloves in near real time, which can enhance workers' safety and protect data privacy. This study used transfer learning methods to monitor PPE compliance and edge computing to improve time efficiency and protect data privacy. Both edge computing-based and cloud computing-based methods were examined and compared pertaining to time efficiency. The results demonstrated how the developed edge computing-based method can improve safety glove detection in a more time-efficient manner while also maintaining data privacy.