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  

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

作　　者：杨鹏 梁雨欣 孔雨新 刘鸣柳 YANG Peng;LIANG Yuxin;KONG Yuxin;LIU Mingliu(School of Electronic Information and Communications,Huazhong University of Science and Technology,Wuhan 430074,China;Electric Power Research Institute,State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430077,China)

机构地区：[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

出　　处：《物联网学报》2024年第4期129-139,共11页Chinese Journal on Internet of Things

基　　金：国家自然科学基金青年科学基金项目(No.62001180);中国科协青年人才托举工程项目(No.2022QNRC001)。

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。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：无人机通信 移动边缘计算 RoI提取 视频编码 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]