An early wildfire detection is essential in order to assess an effective response to emergencies and damages. In this paper, we propose a low-cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance. Since the collection of frame sequences can be difficult and expensive, we propose a virtual environment, based on a cellular model, for the computation of synthetic wildfire smoke sequences. The proposed detection method is tested on both real and simulated frame sequences. The results show that the proposed approach obtains accurate results.
Wildfire smoke detection using computational intelligence techniques enhanced with synthetic smoke plume generation / R. Donida Labati, A. Genovese, V. Piuri, F. Scotti. - In: IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS. SYSTEMS. - ISSN 2168-2216. - 43:4(2013 Jul), pp. 1003-1012. [10.1109/TSMCA.2012.2224335]
Wildfire smoke detection using computational intelligence techniques enhanced with synthetic smoke plume generation
R. Donida LabatiPrimo
;A. GenoveseSecondo
;V. PiuriPenultimo
;F. ScottiUltimo
2013
Abstract
An early wildfire detection is essential in order to assess an effective response to emergencies and damages. In this paper, we propose a low-cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance. Since the collection of frame sequences can be difficult and expensive, we propose a virtual environment, based on a cellular model, for the computation of synthetic wildfire smoke sequences. The proposed detection method is tested on both real and simulated frame sequences. The results show that the proposed approach obtains accurate results.File | Dimensione | Formato | |
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