Forest Fire Detection via Feature Entropy Guided Neural Network |
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Authors: | Zhenwei Guan Feng Min Wei He Wenhua Fang Tao Lu |
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Institution: | 1.Hubei Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430073, China; (Z.G.); (W.F.); (T.L.);2.College of Physics and Electronic Information Engineering, Minjiang University, Fuzhou 350108, China |
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Abstract: | Forest fire detection from videos or images is vital to forest firefighting. Most deep learning based approaches rely on converging image loss, which ignores the content from different fire scenes. In fact, complex content of images always has higher entropy. From this perspective, we propose a novel feature entropy guided neural network for forest fire detection, which is used to balance the content complexity of different training samples. Specifically, a larger weight is given to the feature of the sample with a high entropy source when calculating the classification loss. In addition, we also propose a color attention neural network, which mainly consists of several repeated multiple-blocks of color-attention modules (MCM). Each MCM module can extract the color feature information of fire adequately. The experimental results show that the performance of our proposed method outperforms the state-of-the-art methods. |
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Keywords: | forest fire detection feature entropy convolutional neural network |
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