Image-Based Fire Detection Using Neural Networks

DOI

10.2991/jcis.2006.301How to use a DOI?

Keywords

burning degree estimation, color masking, fire flame detection, neural network

Abstract

An image-based fire detection method using neural networks is proposed in this paper. First, flame color features, based on the HSI color model, are trained by a backpropagation neural network for flame recognition. Then, based on the learned flame color features, regions with fire-like colors are roughly separated from an image. Besides segmenting flame regions, background objects with similar fire colors or resulted from the reflection of fire flames are also separated from the image. In order to get rid of these spurious fire-like regions, the image difference method and the invented color masking technique are applied. Finally, a compact method is devised to estimate the burning degree of fire flames so that users could be informed with a proper warning alarm. The proposed system can achieve 96.47% fire detection rate on average.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wen-Bing Horng
AU  - Jian-Wen Peng
PY  - 2006/10
DA  - 2006/10
TI  - Image-Based Fire Detection Using Neural Networks
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.301
DO  - 10.2991/jcis.2006.301
ID  - Horng2006/10
ER  -