A deep learning-based method for real-time personal protective equipment detection

Abstract

Construction had the most fatal occupational injuries out of all industries due to the high number of annual accidents. There are many solutions to ensure workers’ safety and limit these accidents, one of which is to ensure the appropriate use of appropriate personal protective equipment (PPE) specified in safety regulations. However, the monitoring of PPE use that is mainly based on manual inspection is time consuming and ineffective. This paper proposed a new framework to automatically monitor whether workers are fully equipped with the required PPE. The method based on YOLO algorithm to detect in real-time protective equipment in images. Along with that, we have built a data set of 4400 images of 6 types of common protective equipment at the site for training and system evaluation. Several experiments have been conducted and the results emphasize that the system has demonstrated the ability to detect PPE with high precision and recall in real-time.