简化森林野火监测：利用FLAME航空视频数据集实现轻量高效监测的AI增强无人机
Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring
AI总结：
本文提出一种基于策略网络和帧压缩的轻量高效航空视频理解方法，引入驻留点概念利用未来信息提升精度，在FLAME野火数据集上实现计算成本降低13倍以上且精度提升3%，并能智能筛选显著帧以精炼数据集、加速模型训练。
摘要：
近年来，无人机（UAV）通过分析航空图像，在支持灾害应急响应工作中发挥着日益关键的作用。当前的深度学习模型侧重于提高精度，却往往忽视了无人机有限的计算资源。本研究认识到灾害响应场景中对实时数据处理的需求，提出了一种轻量且高效的航空视频理解方法。我们的方法通过策略网络识别视频中的冗余部分，并利用帧压缩技术消除这些多余信息。此外，我们引入了“驻留点”的概念，在序列策略网络中利用未来信息，从而提升精度。为验证我们的方法，我们采用了野火FLAME数据集。与基线相比，我们的方法将计算成本降低了13倍以上，同时精度提升了3%。此外，我们的方法能够智能地从视频中选择显著帧，从而精炼数据集。这一特性使得复杂模型能够在更小的数据集上有效训练，显著缩短训练过程所花费的时间。
英文摘要：
In recent years, unmanned aerial vehicles (UAVs) have played an increasingly crucial role in supporting disaster emergency response efforts by analyzing aerial images. While current deep-learning models focus on improving accuracy, they often overlook the limited computing resources of UAVs. This study recognizes the imperative for real-time data processing in disaster response scenarios and introduces a lightweight and efficient approach for aerial video understanding. Our methodology identifies redundant portions within the video through policy networks and eliminates this excess information using frame compression techniques. Additionally, we introduced the concept of a `station point,' which leverages future information in the sequential policy network, thereby enhancing accuracy. To validate our method, we employed the wildfire FLAME dataset. Compared to the baseline, our approach reduces computation costs by more than 13 times while boosting accuracy by 3$\%$. Moreover, our method can intelligently select salient frames from the video, refining the dataset. This feature enables sophisticated models to be effectively trained on a smaller dataset, significantly reducing the time spent during the training process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
