Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (1): 89-95.doi: 10.11896/jsjkx.200800034

Special Issue: Intelligent Edge Computing

• Intelligent Edge Computing • Previous Articles     Next Articles

L-YOLO:Real Time Traffic Sign Detection Model for Vehicle Edge Computing

SHAN Mei-jing, QIN Long-fei, ZHANG Hui-bing   

  1. Department of Information Science and Technology,East China University of Political Science and Law,Shanghai 201620,China
    Guangxi Key Lab of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2020-08-05 Revised:2020-12-12 Online:2021-01-15 Published:2021-01-15
  • About author:SHAN Mei-jing,born in 1979,Ph.D,associate professor.Her main research interests include cybercrime and compu-ter forensics and machine learning.
    ZHANG Hui-bing,born in 1976,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include Internet of Things,edge computing and social computing.
  • Supported by:
    National Social Science Fund of China(16BFX085).

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Abstract: In the vehicle edge computing unit,due to the limited resources of its hardware equipment,it becomes more and more urgent to develop a lightweight and efficient traffic sign detection model for vehicle edge computing.This paper proposes a lightweight traffic sign detection model based on Tiny YOLO,which is called L-YOLO.Firstly,L-YOLO uses partial residual connection to enhance the learning ability of lightweight network.Secondly,in order to reduce the false detection and missed detection of traffic signs,L-YOLO uses Gauss loss function as the location loss of boundary box.In the traffic sign detection dataset named TAD16K,the parameter amount of L-YOLO is 18.8M,the calculation amount is 8.211BFlops,the detection speed is 83.3FPS,and the mAP reaches 86%.Experimental results show that the algorithm not only guarantees the real-time performance,but also improves the detection accuracy.

Key words: Convolutional neural network, Object detection, Residual connection, Tiny YOLO, Traffic sign detection, Vehicle edge computing

CLC Number: 

  • TP391
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