Camera-Aided Technology for Underground Mine Safety (CAT-UMS)

The traditional approach to safety clearance monitoring and proper PPE usage in underground mines is time-consuming, costly, and susceptible to negligence. However, automated real-time monitoring can be implemented using computer vision-based approaches. This study aims to develop a system that provides timely information based on the automatic detection of proper PPE use and safety clearance in underground mines. Deep learning and a decentralized computing structure between the cloud and sensing devices (fog technology) are used. We hypothesize that a camera-based system can provide near real-time assessment of proper PPE use and verified clearance to access restricted areas in underground mines.