Real-Time Personal Protective Equipment Compliance Monitoring Using Deep Learning Techniques

This paper, we provide a real-time detection approach that detects Personal Protective Equipment using YOLOv8, a Latest object detection model. It is used to keep an eye on construction sites or industrial environments and confirm whether workers are using appropriate protective gear like hard hats and safety vests. The model receives input from a webcam or image dataset runs a Personal Protective Equipment (PPE) violation detection model on the video stream or images, and sends alerts in real time on Telegram. Our implementation uses Python, OpenCV (Open-Source Computer Vision Library), and the Ultralytics YOLOv8 implementation. This project presents a viable safety compliance monitor that can easily be integrated into a larger system For the Protection and safety of the workers.