Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection
This work addresses efficient disaster response for UAV operators, but it is incremental as it builds on existing compression and detection methods.
The authors tackled real-time wildfire monitoring on UAVs by proposing a two-stage framework that reduces computational costs while maintaining classification accuracy and achieves higher detection accuracy with similar inference time compared to baselines.
Unmanned Aerial Vehicles (UAVs) have become increasingly important in disaster emergency response by enabling real-time aerial video analysis. Due to the limited computational resources available on UAVs, large models cannot be run independently for real-time analysis. To overcome this challenge, we propose a lightweight and efficient two-stage framework for real-time wildfire monitoring and fire source detection on UAV platforms. Specifically, in Stage 1, we utilize a policy network to identify and discard redundant video clips using frame compression techniques, thereby reducing computational costs. In addition, we introduce a station point mechanism that leverages future frame information within the sequential policy network to improve prediction accuracy. In Stage 2, once the frame is classified as "fire", we employ the improved YOLOv8 model to localize the fire source. We evaluate the Stage 1 method using the FLAME and HMDB51 datasets, and the Stage 2 method using the Fire & Smoke dataset. Experimental results show that our method significantly reduces computational costs while maintaining classification accuracy in Stage 1, and achieves higher detection accuracy with similar inference time in Stage 2 compared to baseline methods.