Enhancing Occupational Safety: Real-Time Detection of Protective Equipment

In dangerous environments, such as building sites, maintaining worker safety is essential to reducing accidents and guaranteeing the adherence to safety rules. In order to monitor and enforce compliance with personal protective equipment (PPE) in real time, this study introduces an AI-based Safety Kit Detection system that makes use of computer vision and deep learning. The suggested solution uses YOLOv8, a sophisticated object detection model, to recognize vital safety equipment, including gloves, vests, and helmets. To determine whether PPE is present or not, the system analyzes live video streams, extracts frames, and uses deep learning techniques. It allows for prompt intervention by creating real-time notifications and updating a manager dashboard in the event of non-compliance. This kind of technology improves workplace safety, reduces human inspection errors, and ensures compliance with safety regulations by automating PPE monitoring. The high detection accuracy, robustness under various environmental situations, and real-time processing capacity are all demonstrated by the experimental findings. In the near term, scalability across sectors will be improved, predictive analytics will be incorporated, and detection will be made accessible to more types of PPE.