Abstract
Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature exactor and classifier. Current popular algorithms mainly use convolutional neural networks (CNN) to execute feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fully-connected layers in CNN form a classical neural network classifier, which is trained by conventional gradient descent-based implementations, the generalization ability is limited. The performance could be further improved if other favorable classifiers are used instead and extreme learning machine (ELM) is just the candidate. In this paper, a novel CNN-ELM model is proposed, which integrates the CNN’s terrific capability of feature learning with the outstanding generalization performance of ELM. Firstly CNN learns deep and robust features and then ELM is used as classifier to conduct a fast and excellent classification. Experiments on German traffic sign recognition benchmark (GTSRB) demonstrate that the proposed method can obtain competitive results with state-of-the-art algorithms with less computation time.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant 61075072, and 91220301, and the New Century Excellent Talent Plan under Grant NCET-10-0901.
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Zeng, Y., Xu, X., Fang, Y., Zhao, K. (2015). Traffic Sign Recognition Using Deep Convolutional Networks and Extreme Learning Machine. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_28
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DOI: https://doi.org/10.1007/978-3-319-23989-7_28
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