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Traffic Sign Recognition Based on Attribute-Refinement Cascaded Convolutional Neural Networks

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9916)

Abstract

Traffic sign recognition is a critical module of intelligent transportation system. Observing that a subtle difference may cause misclassification when the actual class and the predictive class share the same attributes such as shape, color, function and so on, we propose a two-stage cascaded convolutional neural networks (CNNs) framework, called attribute-refinement cascaded CNNs, to train the traffic sign classifier by taking full advantage of attribute-supervisory signals. The first stage CNN is trained with class label as supervised signals, while the second stage CNN is trained on super classes separately according to auxiliary attributes of traffic signs for further refinement. Experiments show that the proposed hierarchical cascaded framework can extract the deep information of similar categories, improve discrimination of the model and increase classification accuracy of traffic signs.

Keywords

  • Traffic sign recognition
  • Convolutional Neural Network
  • Attribute supervision
  • Deep learning

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Acknowledgments

This work is supported in part by the National Key Research and Development Plan (Grant No.2016YFC0801005) and the National Natural Science Foundation of China (Grant No.61402463).

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Correspondence to Shiming Ge .

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Xie, K., Ge, S., Ye, Q., Luo, Z. (2016). Traffic Sign Recognition Based on Attribute-Refinement Cascaded Convolutional Neural Networks. In: Chen, E., Gong, Y., Tie, Y. (eds) Advances in Multimedia Information Processing - PCM 2016. PCM 2016. Lecture Notes in Computer Science(), vol 9916. Springer, Cham. https://doi.org/10.1007/978-3-319-48890-5_20

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  • DOI: https://doi.org/10.1007/978-3-319-48890-5_20

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