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

• URL: http://arxiv.org/abs/2501.09465v1
• Date: Thu, 16 Jan 2025 10:56:45 GMT
• Title: RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection
• Authors: Jianrui Shi, Yong Zhao, Zeyang Cui, Xiaoming Shen, Minhang Zeng, Xiaojie Liu,
• Abstract summary: 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.<n>This paper introduces RE-POSE, a framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments.
• Score: 3.2805151494259563
• License: http://creativecommons.org/licenses/by/4.0/
• 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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