In this work, a novel unmanned aerial vehicle (UAV) system to detect and monitor early wildfire is proposed and verified with experimental flight tests on a DJI M300 quadrotor UAV. Several
strategies and algorithms are employed to fuse the on-board sensor information from the visible camera, infrared camera, inertial measurement unit (IMU), and global navigation satellite system
(GNSS). Developed strategies achieve the following functions: wildfire smoke and flame detection, wildfire spot geolocation, visible-infrared image registration, and wildfire local environment 3D reconstruction. For wildfire flame and smoke detection and segmentation, ResNet and attention-gate U-net are trained and deployed, which provide the semantic information for other modules in the proposed system. Simultaneous localization and mapping (SLAM) and multi-view stereo (MVS) algorithms are used to recover the camera poses and estimate the geolocation of the wildfire spot.
With the estimated geolocation information, a mode-based visible-infrared image registration algorithm is developed to decrease the false alarm by fusing the visible and infrared images. After that,
structure from motion (SfM) and MVS are utilized to recover the 3D scene of the local environment around the wildfire. Three independent outdoor experiments are conducted to verify the proposed detection, geolocation, registration, and local environment reconstruction algorithms. DJI M300 UAV together with a H20T camera are used as the platform in those flight testing experiments. The proposed system is proven to have promising applications in wildfire management.