RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

Jianrui Shi; Yong Zhao; Zeyang Cui; Xiaoming Shen; Minhang Zeng; Xiaojie Liu

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arXiv · 2501.09465

RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

Jianrui Shi·Yong Zhao·Zeyang Cui·Xiaoming Shen·Minhang Zeng·Xiaojie Liu

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Abstract

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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Jianrui Shi, Yong Zhao, Zeyang Cui, Xiaoming Shen, Minhang Zeng, Xiaojie Liu. 2025-01-16. RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection. https://arxiv.org/abs/2501.09465

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