In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions.

Wildfire smoke detection using computational intelligence techniques / A. Genovese, R. Donida Labati, V. Piuri, F. Scotti - In: Computational Intelligence for Measurement Systems and Applications (CIMSA), 2011 IEEE International Conference on[s.l] : IEEE, 2011. - ISBN 9781612849249. - pp. 34-39 (( convegno IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) tenutosi a Ottawa nel 2011 [10.1109/CIMSA.2011.6059930].

Wildfire smoke detection using computational intelligence techniques

A. Genovese;R. Donida Labati;V. Piuri;F. Scotti
2011

Abstract

In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions.
computer vision; neural networks; smoke detection; forest fires
Settore INF/01 - Informatica
Institute of Electrical and Electronic Engineers
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/168133
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