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Segmentation of Forest Fire Images Based on Convolutional Neural Networks

Nguyen T. Long, Afanasiev D. Alexander, Nguyen T. Huong


Forest fires is one of the sources of significant damage to the ecosystem and serious environmental pollution. Taiga forest fires in Russia are becoming more serious and complex every year, which leads to the urgent need to use modern technologies to prevent forest fires. This paper proposes the use of artificial intelligence (Deep Learning) for the segmentation of forest fires images. The Convolutional Neural Network (CNN) on cache is proposed to speed up the detection of forest fire processes, which can divide input images based on similarity with previously input images. Because the feature maps extracted from the CNN kernel represent the intensity of features, images with a similar intensity can be classified into the same class. Currently, a preliminary model has been built, which has shown good results on the database of US forest fires. Experiments were performed to
measure and evaluate precision and classification time.


image segmentation, convolutional neural network, deep learning, feature map, forest fire.

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