Laser & Optoelectronics Progress, Volume. 55, Issue 12, 121009(2018)

Traffic Sign Recognition Based on Improved Deep Convolution Neural Network

Yongjie Ma*, Xueyan Li, and Xiaofeng Song
Author Affiliations
  • College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu 730070, China
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    References(17)

    [2] Zhang S F, Zhu T. A method of traffic sign detection and recognition based on HDR technology[J]. Laser & Optoelectronics Progress, 55, 091006(2018).

    [4] Zhong X M, Yu G Z, Ma Y L et al. Research on traffic sign recognition algorithms based on fast regional convolution neural network. C]∥Annual Conference Papers of China Society of Automotive Engineers, 4(2016).

    [5] Tan T Z, Lu J B, Wen J W et al. Traffic sign recognition applying with convolution neural network and RPN[J]. Computer Engineering and Applications, 54, 251-256(2018).

    [9] Zhang Y M, Chang F L, Li N J et al. Modified AlexNet for dense crowd counting[C]∥2017 2 nd International Conference on Computer Science and Engineering, Information Science and Internet Technology , 351-357.

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    Yongjie Ma, Xueyan Li, Xiaofeng Song. Traffic Sign Recognition Based on Improved Deep Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(12): 121009

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    Paper Information

    Category: Image Processing

    Received: Apr. 25, 2018

    Accepted: Jul. 12, 2018

    Published Online: Aug. 1, 2019

    The Author Email: Yongjie Ma (myjmyj@163.com)

    DOI:10.3788/LOP55.121009

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