Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 345-350.doi: 10.11896/jsjkx.201200213

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Speed Limit Sign Recognition Based on LeNet-5 CNN and Color Feature

WANG Ji-min, WEI Yi, ZHOU Yu, SUN Ao, LIU Yuan-sheng   

  1. School of Automation,Wuhan University of Technology,Wuhan 430070,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:WANG Ji-min,born in 1995,postgraduate.His main research interests include image processing and computer vision machine learning.
    WEI Yi,born in 1972,Ph.D,professor.Her main research interests include pattern recognition,machine vision and image data processing.
  • Supported by:
    National Natural Science Foundation of China(51177114) and Hubei Province Technical Innovation Major Project(2019AAA016).

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Abstract: Speed limit sign recognition is an important part of intelligent driving.This research analyzes the problems of existing methods.In order to improve the versatility and accuracy of neural networks on Chinese speed limit signs,in the detection part of speed limit signs,a new screening method based on color space is proposed.In the recognition part of the speed limit sign,the neural network is improved on the basis of the existing LeNet-5 architecture.By fusing the German traffic sign data set (GTSRB) and Tsinghua traffic sign data set (TT100K),a new data set is made and sent to the neural network to train the model after data amplification.Using swish activation function,the optimal recognition accuracy rate obtained on the test set is 99.62%,and the model has strong anti-interference ability and strong practical performance.

Key words: Color space, Convolutional neural network, Data augmentation, Gaussian circle detection, Speed limit sign recognition

CLC Number: 

  • TP391
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