Application of Deep Learning YOLO in IoT System for Personal Protective Equipment Detection

The use of Personal Protective Equipment (PPE) is a critical step in ensuring worker safety in various sectors, including industry, construction, and health. However, violations in using PPE often occur, which can increase the risk of work accidents. This study aims to develop a deep learning-based PPE detection system using the YOLOv8 algorithm. This method was chosen because of its superior ability to detect objects in real time with high accuracy. The training data consists of various images of workers in different work environments, label to recognize types of PPE such as helmets, masks, and safety vests. The developed system was tested on a test dataset to evaluate model performance based on metrics such as confusion matrix, inference speed, and detection error rate. The experimental results show that the YOLOv8 model can detect PPE with an accuracy level of up to 95%. The implementation of this system is expected to be an effective solution in increasing compliance with the use of PPE and preventing work accidents.