Abstract
Due to its large area and rugged terrain, the forest often fails to be detected in time and eventually causes severe losses[1]. Therefore, early detection of forest fires is significant for forest fire protection. The application of deep learning to the classification of smoke and fire in forest images can detect forest conditions more accurately. In this paper, a classification network, named ForestResNet, is proposed to efficiently detect forest conditions, which uses ResNet50[2] as a feature extraction network to achieve rapid and accurate extraction of image feature information. Experimental results show that the proposed network achieves excellent segmentation performance in terms of efficiency and accuracy.
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A post-publication change was made to this article on 13 October 2021 to correct the conference title in the pdf header.