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Edge-Cloud Collaborative UAV Object Detection: Edge-Embedded Lightweight Algorithm Design and Task Offloading Using Fuzzy Neural Network
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AI
DOI：10.1109/TCC.2024.3361858.png)
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Abstract
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
Keywords:
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
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1.8K
Citations:
4.3K