Detection of personal protective equipment through automated systems based on computer vision and machine learning

This scientific paper presents the development and implementation of a system for automatic detection of personal protective equipment (PPE) in the mining industry, using computer vision and machine learning. The model is based on the YOLO v8 object detection architecture and is trained on a dataset of images featuring various types of personal protective equipment. The primary goal of this research is to provide efficient and real-time detection of helmets, protective glasses, ear protectors, safety vests, boots, and gloves in mining environments, where worker safety is of critical importance. The model results demonstrate high accuracy and efficiency, with a mean Average Precision (mAP) of 76.9%, precision of 78.3%, and recall of 71.1%. This system has the potential to be integrated with existing surveillance cameras in mines, thereby enabling automatic monitoring of compliance with safety regulations.