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.

Visible and Infrared Image Fusion of Forest Fire Scenes Based on Generative Adversarial Networks with Multi-Classification and Multi-Level Constraints

“…Convolutional neural networks (CNN) construct deep structural neural networks by training on natural samples, which enables a deeper image feature extraction to maintain the integrity of structural information and the preservation of detailed information in fused images [10]. CNNs have achieved good performance in multifocal images, but their computation is time-consuming, and the fusion performance depends on the characteristics of the training samples [11]. Ma et al innovatively introduced generative adversarial networks (GAN) into the field of image fusion, combining adversarial learning and content-specific loss bootstrapping to achieve the preservation of significant contrast and texture details in fused images in an unsupervised situation [12].…”

FuF-Det: An Early Forest Fire Detection Method under Fog

“…Prescribed fire refers to the deliberate ignition of potential fuels in the forest under the conditions of maximum temperature, relative humidity and wind speed at the threshold required for fire to spread [5] in order to reduce the fuel density in the forest and prevent the occurrence of destructive forest fires [6]. For forest fire monitoring, common methods can be categorized into four types: manual patrols, sensor-based monitoring [7][8][9], fire satellite monitoring [10][11][12][13][14], and UAV monitoring [15][16][17][18]. Manual patrols are inefficient, have limited coverage, and expose humans to potential risks in hazardous environments [19].…”