Desert/Forest Fire Detection Using Machine/Deep Learning Techniques

Mason Davis, Mohammad Shekaramiz

As climate change and human activity increase the likelihood of devastating wildfires, the need for early fire detection methods is inevitable. Although, it has been shown that deep learning and artificial intelligence can offer a solution to this problem, there is still a lot of room for improvement. In this research, two new deep learning approaches to fire detection are developed and investigated utilizing pre-trained ResNet-50 and Xception for feature extraction with a detailed comparison against support vector machine (SVM), ResNet-50, Xception, and MobileViT architectures. Each architecture was tuned utilizing hyperparameter searches and trials to seek ideal combinations for performance. To address the under-representation of desert features in the current fire detection datasets, we have created a new dataset. This novel dataset, Utah Desert Fire, was created using controlled fires and aerial imaging with a DJI Mini 3 Pro drone. The proposed modified ResNet-50 architecture achieved the best performance on the Utah Desert Fire dataset, reaching 100% detection accuracy. To further compare the proposed methods, the popular forest fire detection dataset, DeepFire, was deployed with resulting performance analyzed against most recent literature. Here, our proposed modified Xception model outperformed latest publications attaining 99.221% accuracy. The performance of the proposed solutions show an increase in classification accuracy which can be leveraged for the identification of both desert and forest fires.