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Information 2017, 8(2), 45; doi:10.3390/info8020045

A Shallow Network with Combined Pooling for Fast Traffic Sign Recognition

1
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
2
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 24 February 2017 / Revised: 1 April 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
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Abstract

Traffic sign recognition plays an important role in intelligent transportation systems. Motivated by the recent success of deep learning in the application of traffic sign recognition, we present a shallow network architecture based on convolutional neural networks (CNNs). The network consists of only three convolutional layers for feature extraction, and it learns in a backward optimization way. We propose the method of combining different pooling operations to improve sign recognition performance. In view of real-time performance, we use the activation function ReLU to improve computational efficiency. In addition, a linear layer with softmax-loss is taken as the classifier. We use the German traffic sign recognition benchmark (GTSRB) to evaluate the network on CPU, without expensive GPU acceleration hardware, under real-world recognition conditions. The experiment results indicate that the proposed method is effective and fast, and it achieves the highest recognition rate compared with other state-of-the-art algorithms. View Full-Text
Keywords: traffic sign recognition; CNNs; pooling; ReLU traffic sign recognition; CNNs; pooling; ReLU
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Zhang, J.; Huang, Q.; Wu, H.; Liu, Y. A Shallow Network with Combined Pooling for Fast Traffic Sign Recognition. Information 2017, 8, 45.

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