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Negative-Supervised Cascaded Deep Learning for Traffic Sign Classification

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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Correspondence to Shiming Ge .

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Xie, K., Ge, S., Yang, R., Lu, X., Sun, L. (2015). Negative-Supervised Cascaded Deep Learning for Traffic Sign Classification. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 546. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48558-3_25

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  • DOI: https://doi.org/10.1007/978-3-662-48558-3_25

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