Abstract
Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named “dropout”. The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.
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This paper was presented in part at the CCF Chinese Conference on Computer Vision, Tianjin, 2017. This paper was recommended by the program committee.
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Yin, Sh., Deng, Jc., Zhang, Dw. et al. Traffic sign recognition based on deep convolutional neural network. Optoelectron. Lett. 13, 476–480 (2017). https://doi.org/10.1007/s11801-017-7209-0
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DOI: https://doi.org/10.1007/s11801-017-7209-0