Intelligent Worksite Protective Equipment Compliance Detection Using Deep Neural Networks

Abstract

This research introduces a real-time framework for Personal Protective Equipment (WPE) identification utilizing YOLOv8, an advanced object detection model. The suggested system is intended for the ongoing surveillance of building sites and industrial settings to autonomously confirm worker adherence to obligatory safety equipment, including hardhats and highly visible safety vests. The system analyses input stream from a live camera or pre-existing picture datasets to execute real-time detection of WPE violations in video frames. Upon detecting non-compliance, automated alarm notifications are promptly created and dispatched over the Telegram instant messaging platform, facilitating swift safety intervention. The implementation is constructed with Python, utilizing OpenCV with video processing as well as the YOLOv8 system for object identification and inference. The suggested solution presents an effective scalable, and scalable safety compliance tracking system that can be effortlessly incorporated into current industrial safety overall surveillance infrastructures to improve worker security and workplace safety.