Wildfires pose escalating threats to ecosystems, infrastructure, and human safety. This paper presents an integrated autonomous UAV-based wildfire suppression system designed to execute multiple fire-spot extinguishing missions efficiently. The proposed framework assumes prior detection of wildfire locations and comprises two main modules: a trajectory planning algorithm using dynamic programming and a Linear Quadratic Regulator (LQR) controller for optimal trajectory tracking. The dynamic programming algorithm minimizes the flight distance of the multiple fire-spots firefighting trajectory. The LQR controller is developed based on a linearized model of the quadrotor UAV and ensures accurate trajectory tracking. The effectiveness of the proposed system is demonstrated through MATLAB simulations and validated via outdoor experiments using the DJI M300 UAV platform equipped with a custom-designed water-dropping mechanism. Results confirm the feasibility of the system in suppressing multiple wildfire spots autonomously.

In this paper, rethinking the application of deep neural network models in aerial image-based wildfire perception, a gradient-weighted class activation mapping (Grad-CAM) scheme is studied and tested on the model of you only look once (YOLO) for the drone-image-based wildfire detection. Grad-CAM helps the model to be more explainable and understandable. It also helps engineers and fire-fighters to view the production of target layer(s) of the wildfire detection model. Such a benefit is similar as higher-weighted segmentation models. Therefore, Grad-CAM could be a tool to help designing less-weighted wildfire detection models. It could be also used to support fire-fighters labeling the suspect wildfire area and make decision-making process more efficiently.