Fall Detection and Safety Accessory Monitoring System using Deep Learning

In industrial atmosphere, fall detection and safety accessory monitoring systems are crucial for automating tasks and enhancing worker safety by ensuring proper Personal Protective Equipment (PPE) use. Traditional methods involve streaming video data to centralized cloud-based systems for PPE monitoring, but this relies heavily on bandwidth and is vulnerable to network outages. Utilizing deep learning techniques like the YOLO algorithm with 95% accuracy, the system detects falls and monitors PPE adherence. Four YOLO algorithm versions are compared based on performance metrics. The model, trained on Google Co-laboratory, is deployed on a Raspberry Pi for onsite implementation, providing reliable notifications and fostering a secure work environment.