Edge-assisted UAV onboard video compression and transmission for efficient inference of patrolling tasks

YANG Peng; LIANG Yuxin; KONG Yuxin; LIU Mingliu

doi:10.11959/j.issn.2096-3750.2024.00418

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Edge-assisted UAV onboard video compression and transmission for efficient inference of patrolling tasks

Theory and Technology | 更新时间：2025-01-20

- Edge-assisted UAV onboard video compression and transmission for efficient inference of patrolling tasks
- Chinese Journal on Internet of Things Vol. 8, Issue 4, Pages: 129-139(2024)
- 作者机构：
  
  1.华中科技大学电子信息与通信学院，湖北武汉 430074
  2.国网湖北省电力有限公司电力科学研究院，湖北武汉 430077
- 作者简介：
- 基金信息：
  
  The Young Scientists Fund of the Nation Natural Science Foundation of China(62001180);Young Elite Scientists Sponsorship Program by CAST(2022QNRC001)
- DOI：10.11959/j.issn.2096-3750.2024.00418
  CLC：
- Received：08 October 2024，
  
  Revised：2024-10-19，
  
  Published：10 December 2024
- 稿件说明：
移动端阅览
杨鹏,梁雨欣,孔雨新等.面向高效巡检任务推理的边缘辅助无人机机载视频压缩与传输[J].物联网学报,2024,08(04):129-139.

YANG Peng,LIANG Yuxin,KONG Yuxin,et al.Edge-assisted UAV onboard video compression and transmission for efficient inference of patrolling tasks[J].Chinese Journal on Internet of Things,2024,08(04):129-139.
杨鹏,梁雨欣,孔雨新等.面向高效巡检任务推理的边缘辅助无人机机载视频压缩与传输[J].物联网学报,2024,08(04):129-139. DOI： 10.11959/j.issn.2096-3750.2024.00418.

YANG Peng,LIANG Yuxin,KONG Yuxin,et al.Edge-assisted UAV onboard video compression and transmission for efficient inference of patrolling tasks[J].Chinese Journal on Internet of Things,2024,08(04):129-139. DOI： 10.11959/j.issn.2096-3750.2024.00418.

摘要

面向无人机（UAV

unmanned aerial vehicle）巡检任务采集数据的高效推理，研究了移动边缘计算（MEC

mobile edge computing）辅助的UAV机载视频中感兴趣区域（RoI

region of interest）提取和高效传输问题，以提升UAV在一般巡检任务中采集和分析数据性能。由于UAV机载计算资源有限，提出了一种基于类激活映射（CAM

class activation mapping）的轻量级RoI提取方法，以快速定位包含潜在目标的区域，并将这些RoI高效卸载至边缘服务器进行推理。为应对UAV动态轨迹与网络环境的变化，进一步通过自适应RoI边界框选择算法对UAV采集的RoI进行有效筛选，并利用量化参数（QP

quantization parameter）自适应调整机载视频编码质量，以进一步压缩传输数据量。在此基础上，构建了一个联合RoI边界框选择与自适应编码配置的优化问题，并采用启发式算法求解该优化问题。实验结果表明，该方案能够有效提升检测精度，减少传输数据量，并显著降低系统时延，在基于UAV的一般巡检任务中表现出优异的性能。

Abstract

The problem of region of interest (RoI) extraction and transmission of video frames captured in edge-assisted unmanned aerial vehicle (UAV) systems was investigated to improve the inference performance of patrolling tasks. Due to the limited UAV onboard computational resources

a lightweight RoI extraction method based on class activation mapping (CAM) was proposed

which was able to rapidly locate areas containing patrolling targets. Those RoIs were then transmitted to edge servers for further processing. To address the challenges from dynamic UAV trajectories and fluctuating network conditions

the RoIs collected by UAVs were properly choosen through an adaptive RoI box selection algorithm

followed by adaptive configuration of quantization parameters (QP) of video codec

in order to further compress the transmitted data volume. A joint optimization problem was thus formulated for RoI box selection and adaptive coding configuration

which was solved via a heuristic algorithm. Experimental results demonstrate that

the proposed approach can effectively improve the detection accuracy of patrolling tasks

reduce data transmission volume

and significantly lower system latency

indicating great potential in UAV-based patrolling applications.

关键词

Keywords

references

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