Development of a Smart System for Early Detection of Forest Fires based on Unmanned Aerial Vehicles
Georgi Hristov, Nikolay Zlatov, Plamen Zahariev, Chi Hieu Le, Diyana Kinaneva, Georgi Georgiev, Yavor Yotov, James Gao, Anh My Chu, Ho Quang Nguyen, Le Minh Huynh, Trung Thanh Bui, Mohd Nor Azmi Ab Patar, Jamaluddin Mahmud, Michael S. Packianather
Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 135–140 (2022)
Abstract. The naturally occurring wildfires and the people-related forest fires are events, which in many cases have significant impact on the environment, the wildlife and the human population. The most devastating among these events usually start in unpopulated remote areas, which are difficult to inspect or are not constantly being monitored or observed. This gives the local small-sized fires enough time to evolve into full-scale wide-area disasters, which in turn makes their suppression and extinguishing very difficult. In this paper, we present an autonomous system for early detection of forest fires, named THEASIS-M. The presented system represents a solution that is based on a combination of innovative technologies, including computer vision algorithms, artificial intelligence and unmanned aerial vehicles. In the first part of the study, we provide an overview on the present applications of the UAVs in the forestry domain. The paper then introduces the general architecture of the THEASIS-M system and its components. The system itself is fully autonomous and is based on several different types of UAVs, including a fixed-wing drone, which provides the overall forest monitoring capabilities of the proposed solution, and a rotary-wing UAV that is used for confirmation and monitoring of the detected fire event. The widely used technologies for computer vision and image processing, which are used for the detection of fire and smoke in the real-time video streams sent from the UAVs to the ground control station, are highlighted in the next section of this study. Finally, the experimental tests and demonstrations of the proposed THEASIS-M system are presented and briefly discussed.
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