面向低延迟边缘视觉系统的图神经网络辅助演员-评论家算法

中文摘要 AI 辅助

无人机机载视觉系统被广泛用于各类活动，包括禁飞区监测。在此场景下，配备视觉设备的无人机会将视频流传输至地面服务器，由操作员协助其完成相关活动。视频传输的延迟对操作员协助的有效性有深远影响，但现有多数视频传输技术仍会产生显著的延迟成本。本文提出一种图卷积神经网络辅助的A2C（GCN-Assisted A2C）深度强化学习（DRL）系统模型，用于查找可疑目标的最优像素关联区域。我们将拉格朗日对偶形式与梯度下降相结合，以避免延迟优化过程中出现收敛不足、惩罚过度或不足以及违反约束的问题。该系统模型会从无人机向服务器传输视频帧的子组像素关联区域，而非传输完整视频帧；所提框架利用GCN模型探索像素特征关联组的隐式表示，且GCN会监督A2C模型选择子组以降低传输延迟，进而监督A2C中无人机动作的训练。实验结果表明，与其他DRL模型及现有最优模型相比，GCN辅助的A2C可同时降低无人机视觉系统的视频帧传输延迟与误检率。

英文摘要

UAV on-board vision systems are widely used for different activities, including monitoring in no-fly zones. In this case, the vision-equipped UAV streams a video to a ground server where an operator assists its activities. The latency of video transmission has a profound impact on the effectiveness of the operator assistance. However, most techniques available for video transmission still incur significant latency costs. In this paper, we propose a graph convolutional neural network-assisted (GCN-Assisted A2C) deep reinforcement learning (DRL) system model to find the optimal pixel-correlated area of a suspicious object. We combine the Lagrangian dual form with gradient descent to prevent lack of convergence and over- and under-penalization constraint violation during latency optimization. The proposed system model sends a sub-group pixel-correlated area of the frame from the UAV to the server rather than the transmission of the whole video frame. The proposed framework utilizes the GCN model to explore hidden representations of feature-correlated groups of pixels. Moreover, the GCN supervises the A2C model, which selects a subgroup to enhance transmission latency, thus supervising the training of UAV actions in A2C. Experimental results show that GCN-assisted A2C reduces video frame transmission latency together with false detection rate in UAV vision systems over other DRL and state-of-the-art models.

发表机构

• CISTER Research Center（CISTER研究中心）
• Instituto de Telecomunicações（电信研究所）
• Faculdade de Engenharia, Universidade do Porto（波尔图大学工程学院）
• Kennesaw State University（肯尼索州立大学）
• OmniVision Technologies Inc.（豪威科技公司）
• Pontificia Universidad Católica de Chile（智利天主教大学）

机构由 AI 辅助整理，请以论文原文为准。