Deep Convolutional Neural Networks for Forest Fire Detection
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.