articleIEEE Transactions on Intelligent Transportation SystemsMar 22, 2022GREEN OA

Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

SLSiyuan LiangHWHao WuLZLi ZhenQHQiaozhi HuaSGSahil Garg

Xi’an University of Posts and Telecommunications · Hubei University of Arts and Science · +3 more institutions

Indexed inarxivcrossref

Abstract

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven…

Citation impact

325
total citations
FWCI
28.52
Percentile
100%
References
61
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Authors

8

Topics & keywords

Topics
Keywords
  • Cloud computing
  • Computer science
  • Object detection
  • Enhanced Data Rates for GSM Evolution
  • Convolutional neural network
  • Reliability (semiconductor)
  • Real-time computing
  • Edge computing
UN Sustainable Development Goals
  • Affordable and clean energy
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