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

delete2024-01-01
delete5
PRE
AI
袁
袁亚洲 (Yazhou Yuan) *
S
Shicong Gao
Z
Ziteng Zhang
W
W. Wang
徐
徐哲壮 (Zhezhuang Xu)
刘
刘志新 (Zhixin Liu)
DOI:10.1109/TCC.2024.3361858delete
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摘要

摘要

En 中文
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 (${{\text{E}}<^>{2}}\text{L}$E2L) 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
Keyword:
Object detection
Image edge detection
Autonomous aerial vehicles
Task analysis
Cloud computing
Real-time systems
Collaboration
Attention mechanism
edge-cloud collaborative
fuzzy neural network
object detection
UAV
YOLOv4

期刊

I
IEEE Transactions on Cloud Computing
IF:
5
论文数:
1.8K
被引数:
4.3K

机构

Y
Yanshan University
学者数:
1.7W
论文数: 1.1W
被引数: 1.3W
F
fuzhou university
学者数:
3.3W
论文数: 2.1W
被引数: 31
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