Ensuring Miners’ Safety in Underground Mines Through Edge Computing: Real-Time PPE Compliance Analysis Based on Pose Estimation

Abstract

Safety in underground mining is critically challenged by environmental conditions and the need for rigorous adherence to safety protocols. Draa Sfar, the deepest mine in Morocco, presents extreme conditions that test the effectiveness of Personal Protective Equipment (PPE) compliance. This study addresses the gaps in real-time safety monitoring and compliance in such challenging environments. The primary objective of this research is to enhance PPE compliance detection in underground mines using advanced computer vision techniques. The study aims to develop a system that not only detects PPE but also ensures its proper use through pose estimation. The study involved collecting and annotating a unique dataset from the Draa Sfar mine, characterized by its harsh environmental conditions. Pose estimation was performed using the newly developed You Only Live Once (YOLO) Pose v8 algorithm, tailored for miners in underground settings. For PPE detection—specifically helmets, safety vests, gloves, and boots—we employed and compared several models including YOLO v8, v9, v10, Real-Time Detection Transformer (RT-DETR), and YOLO World. PPE compliance was then assessed by integrating pose estimation keypoints to filter out false detections effectively. The integrated approach successfully identified and verified the use of PPE with high accuracy. Comparative analysis showed that newer versions of YOLO alongside RT-DETR provided substantial improvements in detection rates under varied lighting and spatial conditions prevalent in underground mines. The findings demonstrate that combining pose estimation with advanced object detection frameworks significantly enhances PPE compliance monitoring in underground mines. This dual approach reduces the risk of false positives and ensures a more reliable safety system. By improving the accuracy and reliability of safety equipment detection in one of the most challenging mining environments, this research contributes to reducing occupational hazards and enhancing miner safety. The implications extend to other high-risk industries where environmental conditions complicate safety monitoring.

Keywords

• Personal protective equipment
• edge computing
• pose estimation
• miners’ safety
• underground mines