A Smart System for Personal Protective Equipment Detection in Industrial Environments Based on Deep Learning

The adoption of real-time object detection systems via video streaming analysis is currently exploited in several contexts, from security monitoring to safety prevention. In industrial environments, proper usage of Personal Protective Equipment (PPE) is paramount to ensure workers' safety. However, the use of some types of PPE, such as helmets, is often neglected by workers, especially in indoor areas. Thus, in order to reduce the risks of accidents, real-time video streaming-based monitoring systems may be used to monitor areas in which workers operate and alert them not to wear PPEs via acoustic alarms or visual signals. In case of a remote analysis, there are potential issues related to the high rate of data streams to be transported and analyzed and workers' privacy. In this work, we propose an embedded smart system for real-time PPE detection based on video streaming analysis and deep learning models. We discuss the deployment of different versions of the YOLOv4 network fine-tuned using a public PPE dataset. In the end, we assess the performance of the proposed system in terms of accuracy and latency and of the overall PPE detection procedure.