无人机火灾视频分析新框架，高效省资源。
Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection
- 分两阶段处理：先用策略网络删冗余视频，再检测火源
- 第一阶段降低计算成本，第二阶段实时精准定位火源
- 适合资源受限的无人机平台，尤其适用于野外火灾监测
无人飞行器（UAV）在灾害应急响应中通过空中视频分析发挥越来越重要的作用。由于机载计算资源有限，大型模型难以高效运行。为此，我们提出一种轻量级高效的双阶段框架，用于无人机平台上的野火监测与火源检测。第一阶段利用策略网络识别并剔除冗余视频片段，从而降低计算开销；同时引入站位点机制，融合序列中的未来帧信息以提升预测精度，使该阶段接近实时运行。第二阶段针对被判定含火的帧，应用改进的YOLOv8模型实现在选定帧上的实时火源精确定位。我们在FLAME和HMDB51数据集上评估第一阶段，在火灾与烟雾检测数据集上评估第二阶段。实验结果表明，本方法显著降低计算成本，同时保持第一阶段分类准确率，第二阶段实现高精度检测且支持实时推理。
原文摘要 · Abstract (English)
Unmanned Aerial Vehicles (UAVs) have become increasingly important in disaster emergency response by facilitating aerial video analysis. Due to the limited computational resources available on UAVs, large models cannot be run efficiently for on-board analysis. To overcome this challenge, we propose a lightweight and efficient two-stage framework for 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, thereby reducing computational costs. We also introduce a station point mechanism that incorporates future frame information within the sequential policy network to improve prediction accuracy. This mechanism allows Stage 1 to operate in a near-real-time manner. In Stage 2, for frames classified as containing fire, we apply an improved YOLOv8 model to accurately localize the fire source in real-time on selected frames. We evaluate Stage 1 using the FLAME and HMDB51 datasets, and Stage 2 using the Fire & Smoke Detection Dataset. Experimental results show that our method significantly reduces computational costs while maintaining classification accuracy in Stage 1, and achieves high detection accuracy with real-time inference in Stage 2.
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