Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network

Yuan, Yazhou; Gao, Shicong; Zhang, Ziteng; Wang, Wenye; Xu, Zhezhuang; Liu, Zhixin

doi:10.1109/tcc.2024.3361858

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Zhou et al [26] investigated a UAV-oriented computation offloading problem for the sake of completing its computation task demands with the help of ground edge computing facilities. Yuan et al [27] proposed a novel UAV edge and cloud collaborative framework for the sake of object detection, which attempts to achieve the moving targets in a timely and accurate manner. Ma et al [28] designed a novel task-offloading framework that vehicles' computation jobs could execute natively; otherwise, they were offloaded to UAVs and ECDs.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Li,

Qu,

Liu

et al. 2024

Electronics

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With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource management architecture was proposed in space–air–ground integrated networking (SAGIN). In this manuscript, we established a novel model considering the computation offloading cost constraints of the communication, computing and cache model in the SAGIN. To be specific, the joint optimization problem of collaborative computation offloading and resource management was modeled as a mixed integer nonlinear programming problem. To address this issue, this paper proposed a computation offloading and resource allocation strategy based on deep reinforcement learning (DRL). Differing from traditional methods, DRL does not need a well-established formulation or previous information, and it is capable of revising the strategy adaptively according to the environment. The simulation results demonstrate the proposed approach can achieve the optimal reward values in the case of different terminal device numbers. Furthermore, this manuscript provided the analysis with variant parameters of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Li,

Qu,

Liu

et al. 2024

Electronics

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Optimizing Monocular Driving Assistance for Real-Time Processing on Jetson AGX Xavier

Nguyen,

Nguyen-Ngoc Tran,

Pham

et al. 2024

IEEE Access

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While computer vision and computing technology advances have facilitated advanced driver assistance applications, systems with multi-task design remain highly demanding to operate at high speed on resourceconstrained devices. Our study addresses this challenge by proposing a real-time driver assistance solution specifically developed for a single Jetson AGX Xavier embedded device. It simultaneously performs lane detection, traffic object detection and recognition, and rule-based scene analysis. To achieve high throughput (up to 43 frames per second) without reliance on additional hardware or cloud server, the system exploits Jetson device's specialized AI accelerator and employs various optimization techniques: multithreaded programming, the TensorRT framework, and post-training quantization. The modular design integrates stateof-the-art task-specific methods and ensures adaptation to diverse traffic scenarios across countries as well as future hardware and solutions. Experimental results using a Korean dashcam traffic dataset validated the system's effectiveness and practicality.INDEX TERMS Advanced driver assistance system (ADAS), autonomous driving, edge device, real-time.

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AI-Enabled Collaborative Distributed Computing in Networked UAVs

Mokhtar

2024

IEEE Access

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Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network

Cited by 5 publications

References 29 publications

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

Optimizing Monocular Driving Assistance for Real-Time Processing on Jetson AGX Xavier

AI-Enabled Collaborative Distributed Computing in Networked UAVs

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