Deep Convolutional Neural Networks for Forest Fire Detection
Qingjie Zhang, Jiaolong Xu, Liang Xu, Haifeng Guo
Available Online January 2016.
- https://doi.org/10.2991/ifmeita-16.2016.105How to use a DOI?
- Fire detection, Convolutional Neural Networks, UAVs
- 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.
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 -