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Information 2017, 8(2), 45;

A Shallow Network with Combined Pooling for Fast Traffic Sign Recognition

Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha 410114, China
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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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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