Journal
PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019)
Volume -, Issue -, Pages 2118-2123Publisher
IEEE
DOI: 10.1109/iciea.2019.8833958
Keywords
Forest Fire Detection (FFD); Convolutional Neural Network (CNN); Unmanned Aerial Vehicles (UAVs); Local Binary Pattern (LBP); Image Preprocessing
Funding
- National Natural Science Foundation of China [61573282, 61833013, 61873200]
- Natural Sciences and Engineering Research Council of Canada
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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.
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