用强化学习动态拆分视频帧，提升边缘设备目标检测速度与精度。
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection
- 基于强化学习动态划分视频帧块，按物体分布和模型特性智能分区。
- 相比现有方法，检测准确率更高，推理延迟降低显著。
- 适合对实时性与精度要求高的边缘智能场景，如智慧城市、自动驾驶。
目标检测在智能视频分析中至关重要，广泛应用于自动驾驶、安防和智慧城市建设。然而，由于边缘设备计算资源有限，且深度神经网络（DNN）检测模型对高分辨率视频处理需求高，实现实时检测面临巨大挑战。传统方法如输入下采样或网络上采样常以牺牲检测精度换取速度，或导致更高的推理延迟。为此，本文提出RE-POSE框架，一种基于强化学习的动态分区与边缘卸载机制，旨在优化资源受限边缘环境下的精度-延迟权衡。其核心为强化学习驱动的动态聚类算法（RL-DCA），根据物体分布与DNN计算特性将视频帧划分为非均匀块；同时采用并行边缘卸载方案，将这些块分发至多个边缘服务器进行并发处理。实验表明，RE-POSE显著提升了检测准确率并降低了推理延迟，优于现有方法。
原文摘要 · Abstract (English)
Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)-Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods.
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