Thermal–RGB Fusion for Micro-UAV Wildfire Perimeter Tracking with Minimal Comms

This study introduces a lightweight perimeter-tracking method designed for micro-UAV teams operating over wildfire environments under limited bandwidth conditions. Thermal image frames generate coarse hot-region masks through adaptive thresholding and morphological refinement, while RGB frames contribute edge cues and suppress texture-related false detections using gradient-based filtering. A rule-level merging strategy selects boundary candidates and simplifies them via the Ramer–Douglas–Peucker algorithm. The system incorporates periodic beacons and an inertial feedback loop that maintains trajectory stability in the presence of GPS degradation. The guidance loop targets sub-50 ms latency on embedded System-on-Chip (SoC) platforms by constraining perframe pixel operations and precomputing gradient tables. Small-scale simulations demonstrate reductions in average path length and boundary jitter compared to a pure edge-tracking baseline, while maintaining environmental coverage measured through intersection–merge analysis. Battery consumption and computational utilization confirm the feasibility of achieving 10–15 m/s forward motion on standard micro platforms. This approach enables rapid deployment in the field, requiring robust sensing and minimal communications for emergency reconnaissance applications.