YOLO-Based Personal Protective Equipment Monitoring System for Workplace Safety

Occupational health and safety are of paramount importance in industrial environments and various work fields. In this context, tracking personal protective equipment (PPE) is highly necessary. This article investigates the performance and application of deep learning-based object detection models to enhance the accuracy and speed of tracking personal protective equipment for ensuring occupational health and safety. These models detect personal protective equipment in images, enabling monitoring of their correct usage and intervention when necessary. The study aims to minimize damage resulting from accidents through the use of protective equipment and to prevent possible accidents. In our study, a dataset consisting of 2581 images, encompassing different workplace environments and workers, was prepared. This dataset was evaluated for performance using deep learning models. Popular deep-learning models such as YOLO-NAS, YOLOv8, and YOLOv9 were utilized in the comparisons. During the training of the models, the number of epochs was kept consistent for fair comparison. Upon examining the results, it is observed that the YOLO-NAS and YOLOv9 models generally exhibit similar and high performance.