Enhancing Safety and Efficiency of Underground Mining Operations Using Vision-Based Systems

Abstract

This thesis investigates the design and deployment of vision-assisted monitoring and alert systems to improve safety and efficiency in underground mining operations. The research integrates advanced computer vision techniques, including object detection, pedestrian tracking, pose estimation, line detection, and Kalman filtering, for real-time operations on edge devices. Two main applications were developed and optimized: a loader monitoring system that tracks loading cycles and boom poses to provide operators with visual feedback, and a pedestrian alert system that combines detection and pose estimation to enhance safety around jumbo drills. Both systems were implemented and tested in realistic underground environments or similar settings, demonstrating their capacity to improve operational efficiency and situational awareness. This work was carried out closely with industry partners, where the focus was not on setting fixed quantitative benchmarks but on delivering systems that operators and managers found useful and reliable. Instead of relying on controlled experiments or predefined metrics, the systems were shaped through an iterative cycle of design, deployment, testing, and feedback. This process often required trade-offs, such as choosing robustness and usability over purely numerical performance gains, but it ensured that the outcomes were relevant to day-to-day operations. Beyond technical development, the experience also highlighted the importance of communication with end-users, as their input directly guided adjustments to system functionality and interface design. By combining modern computer vision methods with field deployment, this thesis contributes not only practical tools for safer and more efficient mining operations, but also insights into how advanced algorithms can be adapted for adoption in real-world industrial settings. These lessons extend beyond mining, offering guidance for similar applications in other safety-critical and resource-constrained environments.