Chapter
Apr 26, 2012
An Improved ANN Algorithm for Traffic Sign Recognition
Authors: Qinfang Chai [email protected], Xianqiao Chen [email protected], and Pinfu Yang [email protected]Author Affiliations
Publication: ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation
Abstract
An automatic traffic sign recognition system can help drivers operate the vehicle properly. Most existing systems include a detection phase and a classification phase. In this paper, a new classification method is presented based on an improved ANN algorithm for recognizing traffic signs. When the ANN algorithm is chosen to for traffic sign recognition, the key factor is to get the right weights in neural networks. Traditionally, the weights were solved by training the neural networks with a give samples set. But in most of the cases the convergence for the training is very slow, even it becomes divergence. In this paper, an improved BP neural networks algorithm was proposed. Compared with the old algorithm, a dynamic learning rate was used to get an optimization learning rate instead of a fixed learning rate. Combined the moment features, GSC features, experiments show that the iterative times for ANN training is reduced evidently.
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© 2011 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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Associate Professor, Zhijiang Institute of Communications, Zhijiang Hangzhou 311112, China.E-mail: [email protected]
Professor, School of Comp., Wuhan Univ. of Tech., Wuhan 430063, China.E-mail: [email protected]
Senior Engineer, Center of Measure, Changjiang Waterway Bureau, Wuhan 430063, China.E-mail: [email protected]
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