An Improved ANN Algorithm for Traffic Sign Recognition

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 artificial neural network (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.

• Record URL:
  • https://doi.org/10.1061/41177(415)243
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784411773
• Supplemental Notes:
  • Copyright © 2011 ASCE
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Chai, Qinfang
  • Chen, Xianqiao
  • Yang, Pinfu
• Conference:
  • First International Conference on Transportation Information and Safety (ICTIS)
  • Location: Wuhan , China
  • Date: 2011-6-30 to 2011-7-2
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: pp 1929-1937
• Monograph Title: ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation

Subject/Index Terms

• TRT Terms: Algorithms; Classification; Machine learning; Neural networks; Optimization; Traffic signs
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I70: Traffic and Transport;

Filing Info

• Accession Number: 01480676
• Record Type: Publication
• ISBN: 9780784411773
• Files: TLIB, TRIS, ASCE
• Created Date: May 3 2013 8:55AM