Unmanned Aerial Vehicle Assisted Forest Fire Detection Using Deep Convolutional Neural Network

Abstract

Disasters may occur at any time and place without little to no presage in advance. With the development of surveillance and forecasting systems, it is now possible to forebode the most life-threatening and formidable disasters. However, forest fires are among the ones that are still hard to anticipate beforehand, and the technologies to detect and plot their possible courses are still in development. Unmanned Aerial Vehicle (UAV) image-based fire detection systems can be a viable solution to this problem. However, these automatic systems use advanced deep learning and image processing algorithms at their core and can be tuned to provide accurate outcomes. Therefore, this article proposed a forest fire detection method based on a Convolutional Neural Network (CNN) architecture using a new fire detection dataset. Notably, our method also uses separable convolution layers (requiring less computational resources) for immediate fire detection and typical convolution layers. Thus, making it suitable for real-time applications. Consequently, after being trained on the dataset, experimental results show that the method can identify forest fires within images with a 97.63% accuracy, 98.00% F1 Score, and 80% Kappa. Hence, if deployed in practical circumstances, this identification method can be used as an assistive tool to detect fire outbreaks, allowing the authorities to respond quickly and deploy preventive measures to minimize damage.