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Negative-Supervised Cascaded Deep Learning for Traffic Sign Classification

  • Kaixuan Xie
  • Shiming GeEmail author
  • Rui Yang
  • Xiang Lu
  • Limin Sun
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 546)

Abstract

In this paper, we propose a novel deep learning framework for object classification called negative-supervised cascaded deep learning. There are two hierarchies in our cascaded method: the first one is a convolutional neural network trained on positive-only samples, which is used to select supervisory samples from a negative library. The second one is inherited from the trained first CNN. It is trained on positive and negative samples, which are selected from domain related database by utilizing negative-supervised mechanism. Experiments are applied this idea to traffic sign classification using two classic convolutional neural networks, LeNet-5 and AlexNet as baselines. Classification rates improved by \(0.7\%\) and \(1.1\%\) with LeNet-5 and AlexNet respectively, which demonstrates the efficiency and superiority of our proposed framework.

Keywords

Convolutional neural network Deep learning Negative-supervised Object classification Traffic sign classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Kaixuan Xie
    • 1
    • 2
  • Shiming Ge
    • 2
    Email author
  • Rui Yang
    • 1
    • 2
  • Xiang Lu
    • 2
  • Limin Sun
    • 2
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.Beijing Key Laboratory of IOT Information SecurityInstitute of Information Engineering, Chinese Academy of SciencesBeijingChina

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