YOLO-based miner detection using thermal images in underground mines.

Details

• Personal Author:
  Addy C ; Awuah-Offei K ; Nadendla VSS
• Description:
  Well-designed and effective in-mine robots can expedite miner self-rescue during emergencies and reduce fatalities. These in-mine robots for miner self-rescue can carry out diverse tasks such as scouting (including object detection and autonomous navigation), and payload delivery. However, robots that can effectively detect humans in a dark underground mine do not yet exist. This paper investigates challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time, in low-light conditions. The research team collected 500 thermal images in the Missouri University of Science & Technology Experimental Mine with the help of student volunteers using the FLIR TG 297 infrared camera, which they pre-processed and split into training and validation datasets with 450 and 50 images, respectively, using tenfold cross-validation. The research retrained two state-of-the-art, real-time object detection models, namely YOLOv5 (You Only Look Once version 5), and YOLOv8 (You Only Look Once version 8), using transfer learning techniques on the training dataset for 50 epochs. On the validation dataset, the re-trained YOLOv8 outperforms the re-trained YOLOv5. These trained models as well as the original models were then applied to a simulated mine fire emergency to assess their performance in emergency situations. The results show that the mAP of the YOLOv8 variants improved drastically after transfer learning. For instance, YOLOv8n improved from 13.90 to 74.70%. And that of the YOLOv5 variants improved significantly. For instance, YOLOv5n improved from 42.10 to 68.30%. [Description provided by NIOSH]
• Subjects:
  Mining Occupational Health Safety
• Keywords:
  Author Keywords: Object Detection; Miner Detection; Thermal Image; YOLO; Underground Mine Underground Mines; Robotics; Mine Fires; Mine Rescue; Algorithms; Imaging Techniques; Emergency Egress; Emerging Technology;
• ISSN:
  2524-3462
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Kansas City Region [OSHA] ; Missouri
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• NIOSHTIC Number:
  nn:20070932
• Citation:
  Min Metall Explor 2025 Apr; :[Epub ahead of print]
• Contact Point Address:
  Cyrus Addy, Department of Mining and Explosive Engineering, Missouri University of Science and Technology, Rolla, MO, 65409, USA
• Email:
  ca8mc@mst.edu
• Federal Fiscal Year:
  2025
• NORA Priority Area:
  Mining
• Performing Organization:
  Missouri University of Science and Technology
• Peer Reviewed:
  True
• Start Date:
  20210901
• Source Full Name:
  Mining, Metallurgy & Exploration
• End Date:
  20250831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7dc847c09aedf140bc731323e8446b4703641ae07d6ad9e7b7238120dab30b8f7dcaa9b938d34d035f87873a0fe7fec262d5d13dcaf290925445e82b6de3b57d
• Download URL:
  https://stacks.cdc.gov/view/cdc/248327/cdc_248327_DS1.pdf
• File Type:
  [PDF - 168.02 KB ]