UAV Image-based Forest Fire Detection Approach Using Convolutional Neural Network

Forest fires are very dangerous. Once they become disasters, it is very difficult to extinguish. In this paper, an unmanned aerial vehicle (UAV) image-based forest fire detection approach is proposed. Firstly, the local binary pattern (LBP) feature extraction and support vector machine (SVM) classifier are used for smoke detection, so as to make a preliminary discrimination of forest fire. In order to accurately identify it in the early stage of the fire, according to the convolutional neural network (CNN), it has the characteristics of reducing the number of parameters and improving the training performance through local receptive domain, weight sharing and pooling. This paper proposes another method for detecting forest fires in convolutional neural networks. Image preprocessing operations such as histogram equalization and smooth low-pass filtering are performed prior to inserting the image into the CNN network. The effectiveness of the proposed method is verified by detecting real forest fire images.