PPE Detection with AI: Real-Time Worker Safety Monitoring

The text outlines a comprehensive guide to developing a real-time Personal Protective Equipment (PPE) detection system using Roboflow, aimed at improving safety compliance on construction sites. By leveraging computer vision and a custom-trained RF-DETR object detection model, the system can automatically identify whether workers are wearing helmets and vests, providing a live count of safe and unsafe workers directly on video feeds. The process involves forking a PPE dataset from Roboflow Universe, applying preprocessing and data augmentation techniques, and training the model to detect key PPE elements. A workflow is constructed using Roboflow's drag-and-drop interface to integrate detection, tracking, visualization, and safety counting, enhanced by ByteTrack for consistent worker identification across frames. The result is an efficient, scalable tool for monitoring worker safety, with potential for further customization and deployment in real-world environments.