Compliance of head-mounted personal protective equipment by using YOLOv5 object detector

Abstract

Workplace safety is a scientific discipline that has been constantly evolving along with industrial development. Nowadays technological progress of tools and materials used in industry, in addition to all the positive impacts, increase the probability of injuries of the operators that use them. Consequently, there are industry standards and recommendations that specify appropriate personal protective equipment (PPE) for certain workplaces. Although every company is able to provide protective equipment for its employees, the major challenge is the compliance and control of their proper use. The aim of this study was to assess the possibility of applying artificial intelligence and deep learning techniques for automated PPE compliance, which could help in taking preventive action with the aim of reducing injuries caused due to non-use or misuse of prescribed PPEs. The obtained results showed that the YOLOv5 algorithm achieved high precision (average 0.857) for the detection of various types of head-mounted personal protective equipment. Accordingly, there is a high potential for future use of such tools in improving workplace safety and PPE compliance. Potential users of the application based on this recognition algorithm would be companies which regulations define the type of PPEs that have to be used at a certain working position.