边缘云协作无人机目标检测：使用模糊神经网络的边缘嵌入式轻量级算法设计和任务卸载

IF 5.3 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Cloud Computing Pub Date : 2024-02-05 DOI:10.1109/TCC.2024.3361858

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

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引用次数: 0

摘要

随着人工智能和无人机（UAV）技术的快速发展，基于人工智能的无人机越来越多地应用于各种工业和民用领域。本文提出了一种用于无人机目标检测的分布式边缘-云协作框架，旨在实现对地面移动目标的实时、准确检测。该框架将边缘嵌入式轻量级（${{text{E}}^{2}}\text{L}$）目标算法与注意力机制相结合，在保持高精度的同时实现了边缘嵌入式设备上的实时目标检测。此外，基于模糊神经网络的决策机制促进了边缘端和云端之间的自适应任务分配。实验结果表明，与 YOLOv4 相比，所提算法在边缘侧英伟达 Jetson Xavier NX 上的运行率有所提高，而且分布式边缘-云协作框架在速度和准确性方面的表现优于传统的边缘计算或云计算算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

With the rapid development of artificial intelligence and Unmanned Aerial Vehicle (UAV) technology, AI-based UAVs are increasingly utilized in various industrial and civilian applications. This paper presents a distributed Edge-Cloud collaborative framework for UAV object detection, aiming to achieve real-time and accurate detection of ground moving targets. The framework incorporates an Edge-Embedded Lightweight (

${{\text{E}}^{2}}\text{L}$

) object algorithm with an attention mechanism, enabling real-time object detection on edge-side embedded devices while maintaining high accuracy. Additionally, a decision-making mechanism based on fuzzy neural network facilitates adaptive task allocation between the edge-side and cloud-side. Experimental results demonstrate the improved running rate of the proposed algorithm compared to YOLOv4 on the edge-side NVIDIA Jetson Xavier NX, and the superior performance of the distributed Edge-Cloud collaborative framework over traditional edge computing or cloud computing algorithms in terms of speed and accuracy

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来源期刊

IEEE Transactions on Cloud Computing Computer Science-Software

CiteScore

9.40

自引率

6.20%

发文量

167

期刊介绍： The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.