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Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring

Lemeng Zhao, Junjie Hu, Jianchao Bi, Yanbing Bai, Erick Mas, Shunichi Koshimura

TL;DR

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 and introduces the concept of a station point, which leverages future information in the sequential policy network, thereby enhancing accuracy.

Abstract

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.

Streamlining Forest Wildfire Surveillance: AI-Enhanced UAVs Utilizing the FLAME Aerial Video Dataset for Lightweight and Efficient Monitoring

TL;DR

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 and introduces the concept of a station point, which leverages future information in the sequential policy network, thereby enhancing accuracy.

Abstract

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.
Paper Structure (27 sections, 10 equations, 4 figures, 6 tables)

This paper contains 27 sections, 10 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Method
  4. Network Architecture
  5. Overview
  6. Clip Mixup
  7. Feature Extraction Network
  8. Policy Network
  9. Frame Selection
  10. Gumbel Softmax Trick
  11. Loss Function
  12. Experiments
  13. Experiment Setup
  14. FLAME Dataset
  15. HMDB51 Dataset
  16. ...and 12 more sections

Figures (4)

  • Figure 1: Example of ACCSampler for frame consolidation.
  • Figure 2: The architecture of AccSampler. Frame selection is static and based on the distribution produced by $\pi$
  • Figure 3: Comparison of frame selection methods at various frame rates.
  • Figure 4: Comparison of different calculation methods for S.