Detection for Triangle Traffic Sign Based on Neural Network

  • Shuang-dong Zhu
  • Yi Zhang
  • Xiao-feng Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


This literature critically explains the intelligent method for detection of traffic signs. This method uses a particular color and shape for the detection of traffic signs, as an example, we used red color down triangle shape traffic sign, to explain this method. This method is mainly carried out in four steps, which are as follows. First, convert RGB color space to HIS color space, and extract pixels with red color. Then perform LOG mask operation on the pixels got from step 1, for the detection of edges. By using neural network, we determine the angle pixels, and at the same time, we also determine on which specific angle the pixel is. And finally we detect the traffic sign by using the information of shape. We used 20 different images from different scenes to test this method, and the percentage of correctness is 100%.


Traffic Sign Intelligent Transportation System Colour Distortion Traffic Scene Traffic Sign Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhu, S.: Image Detection and Processing for ITS. In: Proceedings of 2000 Chinese Conference on Measurement, Beijing, China, pp. 512–516 (2000)Google Scholar
  2. 2.
    Zhu, S.: Two Hierarchy Classifier for Recognition of Traffic Signs Based on Neural Network. In: Fifth World Congress on Intelligent Control and Automation(WCICA 2004), Conference Proceedings of WCICA 2004, Hangzhou, China, vol. 6, pp. 5302–5306 (2004)Google Scholar
  3. 3.
    Zhu, S.: The Classification of Traffic Sign Base on Fuzzy Characteristics Training set. In: Fifth World Congress on Intelligent Control and Automation (WCICA 2004), Conference Proceedings of WCICA 2004, Hangzhou, China, vol. 6, pp. 5266–5270 (2004)Google Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley, Reading (2002)Google Scholar
  5. 5.
    Marr, D., Hildreth, E.: Theory of Edge Detection. Proc, R. Sol., Long. 207, 187–217 (1980)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shuang-dong Zhu
    • 1
  • Yi Zhang
    • 2
  • Xiao-feng Lu
    • 1
  1. 1.College of Information Science and TechnologyNingbo UniversityChina
  2. 2.Kristianstad UniversityKristianstadSweden

Personalised recommendations