Electronic Science and Technology ›› 2019, Vol. 32 ›› Issue (11): 28-32.doi: 10.16180/j.cnki.issn1007-7820.2019.11.006

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Traffic Sign Recognition Method Based on Improved Convolutional Neural Network

YUAN Xiaoping,WANG Gang,WANG Yefeng,WANG Zheyuan,SUN Hui   

  1. School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221000,China
  • Received:2018-11-17 Online:2019-11-15 Published:2019-11-15
  • Supported by:
    Natural Science Foundation of Jiangsu(BK20170278)


Aiming at the low recognition rate of small scale traffic signs in intelligent transportation system, an improved convolution neural network method for traffic sign recognition was proposed in this paper.This method could improve the detection rate of small-scale traffic signs by adding an optimized RPN network to the low-level feature map of Faster R-CNN algorithm.In addition, Max Pooling method was used to fully fuse the local details and global semantic features of the image. The experimental results on TT-100K data set showed that the proposed method could significantly improve the recognition rate of small-scale traffic signs.

Key words: deep learning, trafficsign recognition, convolutional neural network, Faster R-CNN, RPN, feature fusion

CLC Number: 

  • TP391.41