4.7 Article

Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine-The AMICOS Project

Journal

REMOTE SENSING
Volume 13, Issue 1, Pages -

Publisher

MDPI
DOI: 10.3390/rs13010069

Keywords

deep underground mine; rescue action; UGV robotic system; infrared thermography; human detection

Funding

  1. European Institute of Innovation and Technology (EIT), a body of the European Union, under the Horizon 2020
  2. EU Framework Programme for Research and Innovation
  3. EIT RawMaterials GmbH [19018]

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Rescue actions in underground mines are highly risky and may involve advanced technologies to deal with potential hazards such as earthquakes, gas leaks, and high temperatures. The testing of a UGV robotic system developed within the AMICOS project showed promising results in human detection efficiency, and future work will focus on testing the technology in deep copper ore mines.
Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.

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