Forest Fires Segmentation using Deep Convolutional Neural Networks

Forest fires are among the most dangerous type of natural disasters since they affect numerous aspects of life, such as natural ecosystems, economy, and human lives. Various vision-based fire detection methods have been proposed to segment fire pixels and detect fire at an early stage. The challenge here is to overcome the limitations of the majority of these methods mainly false detection of fire pixels. For such, we propose in this paper, three deep convolutional networks, U-Net, U-2-Net, and EfficientSeg to segment forest fire pixels and detect fire areas. One of our main contributions is the variation of loss functions of all models. The three models show an excellent performance in terms of accuracy and F1-score, and proved their reliability to segment fire pixels and detect the precise shape of forest fire areas.