Proceedings of the 2016 International Forum on Management, Education and Information Technology Application

Deep Convolutional Neural Networks for Forest Fire Detection

Authors
Qingjie Zhang, Jiaolong Xu, Liang Xu, Haifeng Guo
Corresponding Author
Qingjie Zhang
Available Online January 2016.
DOI
https://doi.org/10.2991/ifmeita-16.2016.105How to use a DOI?
Keywords
Fire detection, Convolutional Neural Networks, UAVs
Abstract
We proposed a deep learning method for forest fire detection. We train both a full image and fine grained patch fire classifier in a joined deep convolutional neural networks (CNN). The fire detection is operated in a cascaded fashion, ie the full image is first tested by the global image-level classifier, if fire is detected, the fine grained patch classifier is followed to detect the precise location of fire patches. Our fire patch detector obtains 97% and 90% detection accuracy on training and testing datasets respectively. To facilitate the evaluation of various fire detectors in the community, we build a fire detection benchmark. According to our best knowledge, this is the first one with patch-level annotations.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Forum on Management, Education and Information Technology Application
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
January 2016
ISBN
978-94-6252-166-7
ISSN
2352-5398
DOI
https://doi.org/10.2991/ifmeita-16.2016.105How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Qingjie Zhang
AU  - Jiaolong Xu
AU  - Liang Xu
AU  - Haifeng Guo
PY  - 2016/01
DA  - 2016/01
TI  - Deep Convolutional Neural Networks for Forest Fire Detection
BT  - 2016 International Forum on Management, Education and Information Technology Application
PB  - Atlantis Press
SN  - 2352-5398
UR  - https://doi.org/10.2991/ifmeita-16.2016.105
DO  - https://doi.org/10.2991/ifmeita-16.2016.105
ID  - Zhang2016/01
ER  -