RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection
This work addresses the problem of real-time object detection for applications like autonomous driving and smart cities, representing an incremental improvement over conventional strategies.
The paper tackles the challenge of achieving real-time object detection on edge devices with limited computational resources by introducing RE-POSE, a reinforcement learning-based framework for partitioning and offloading video frames, which significantly enhances detection accuracy and reduces inference latency compared to existing methods.
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.