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Software integration for human detection in mining UAV systems.

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Mining is one of the main economic sectors in South Africa. Mining activity contains hazards such collapsing of structures, presence of dangerous gases, accidental explosions and fires. Even though most of these hazards are identified and minimized sometimes accidents occur. These accidents lead to human injuries, direct fatalities and fatalities resulting from delays in victims getting medical attention as a result of delays in search and rescue missions. The rescue missions in underground mines present challenges where rescuers are not sure which locations are victims in, what the area conditions like in the rescue path. A quad rotor unmanned aerial vehicle (UAV) for search and rescue missions is presented. The UAV is controlled from a remote location over Wi-Fi. The communication allows data relay to the ground control station. The communication system is tested on the university’s Wi-Fi network. The UAV also contains a vision system that contains a human detection algorithm to give an indication of human presence to rescuers. The human detection system is based on Haar- Cascade classifiers. The model developed was found to have a false alarm rate of 5×10-3% after training. The model was further tested on streaming data and the overall average positive human detection was found to be 97 %. In the same tests overall false average detection was found to be 2.5 %. The video feed is streamed from the UAV to the ground station (GS) and the flight control instructions are sent to the UAV from the GS via Wi-Fi.


Thesis (M.Sc.Eng)-University of KwaZulu-Natal, Durban, 2013.


Mining engineering--South Africa., Mechatronics--South Africa., Drone aircraft--South Africa., Detectors--South Africa., Theses--Mechanical engineering.