A Comprehensive Dataset for Underground Miner Detection in Diverse Scenario
This work addresses safety challenges in underground mining by providing a dataset for developing thermal-based detection systems, though it is incremental as it focuses on data creation rather than new methods.
The paper tackles the lack of comprehensive training datasets for miner detection in underground mining by presenting a novel thermal imaging dataset, and it evaluates state-of-the-art object detection algorithms like YOLOv8 and RT-DETR to establish baseline performance metrics for this application.
Underground mining operations face significant safety challenges that make emergency response capabilities crucial. While robots have shown promise in assisting with search and rescue operations, their effectiveness depends on reliable miner detection capabilities. Deep learning algorithms offer potential solutions for automated miner detection, but require comprehensive training datasets, which are currently lacking for underground mining environments. This paper presents a novel thermal imaging dataset specifically designed to enable the development and validation of miner detection systems for potential emergency applications. We systematically captured thermal imagery of various mining activities and scenarios to create a robust foundation for detection algorithms. To establish baseline performance metrics, we evaluated several state-of-the-art object detection algorithms including YOLOv8, YOLOv10, YOLO11, and RT-DETR on our dataset. While not exhaustive of all possible emergency situations, this dataset serves as a crucial first step toward developing reliable thermal-based miner detection systems that could eventually be deployed in real emergency scenarios. This work demonstrates the feasibility of using thermal imaging for miner detection and establishes a foundation for future research in this critical safety application.