UAV-FlameNet: A Lightweight and High-Precision Flame Detection Model for UAV Aerial Fire Monitoring.

Introduction: Early flame detection via Unmanned Aerial Vehicles (UAVs) is crucial for wildfire prevention. However, extreme small scales, non-rigid morphological distortions, and severe background interference in aerial imagery, coupled with limited edge-computing power, pose significant challenges. Methods: This study proposes UAV-FlameNet, a lightweight detection algorithm based on YOLOv11. Three innovations are introduced: (1) ADown lossless downsampling to preserve faint flame features; (2) Dynamic Cascaded Flame Aggregation (DCFA) integrating deformable convolutions and variance-aware attention to model thermal radiation and suppress glares; (3) MPDIoU loss to accelerate bounding box convergence. Results: On the LAWD dataset, UAV-FlameNet achieves 83.2% mAP@0.5 with 2.72 M parameters and 5.66 GFLOPs, outperforming YOLOv11 by 2.2% while reducing computational load by 12.1%. The model runs at 121 FPS on GPU and 11.7 FPS on CPU. Discussion: AV-FlameNet achieves a Pareto-optimal balance among accuracy, robustness, and deployment efficiency, providing a reliable solution for UAV-borne real-time fire early warning systems.