A forest fire smoke detection model combining convolutional neural network and vision transformer

Abstract

Forest fires seriously jeopardize forestry resources and endanger people and property. The efficient identification of forest fire smoke, generated from inadequate combustion during the early stage of forest fires, is important for the rapid detection of early forest fires. By combining the Convolutional Neural Network (CNN) and the Lightweight Vision Transformer (Lightweight ViT), this paper proposes a novel forest fire smoke detection model: the SR-Net model that recognizes forest fire smoke from inadequate combustion with satellite remote sensing images. We collect 4,000 satellite remote sensing images, 2,000 each for clouds and forest fire smoke, from Himawari-8 satellite imagery located in forest areas of China and Australia, and the image data are used for training, testing, and validation of the model at a ratio of 3:1:1. Compared with existing models, the proposed SR-Net dominates in recognition accuracy (96.9%), strongly supporting its superiority over benchmark models: MobileNet (92.0%), GoogLeNet (92.0%), ResNet50 (84.0%), and AlexNet (76.0%). Model comparison results confirm the accuracy, computational efficiency, and generality of the SR-Net model in detecting forest fire smoke with high temporal resolution remote sensing images.