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Neural Network based Road Sign Recognition

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 50 - Number 10
Year of Publication: 2012
Authors:
Sanjit Kumar Saha
Dulal Chakraborty
Md. Al-amin Bhuiyan
10.5120/7810-0946

Sanjit Kumar Saha, Dulal Chakraborty and Md. Al-amin Bhuiyan. Article: Neural Network based Road Sign Recognition. International Journal of Computer Applications 50(10):35-41, July 2012. Full text available. BibTeX

@article{key:article,
	author = {Sanjit Kumar Saha and Dulal Chakraborty and Md. Al-amin Bhuiyan},
	title = {Article: Neural Network based Road Sign Recognition},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {50},
	number = {10},
	pages = {35-41},
	month = {July},
	note = {Full text available}
}

Abstract

A recent surge of interest is to recognize Road Signs. Signs are visual languages that represent some special circumstantial information of environment. They provide important information for guiding, warning people to make their movements safer, easier and more convenient. This thesis presents a hybrid neural network solution for Road sign recognition which combines local image sampling and artificial neural network. The method is based on BAM for dimensional reduction and multi-layer perception with backpropagation algorithm has been used for training the network. It has been found from practical observations that the number of iterations required to train the network is enormous. The capability of recognition of a neural network increases with increasing the training accuracy. For this process each sign is converted to a designated M×N feature matrix. These feature matrices of signs are then fed into the neural network as input patterns. The neural network is trained with the set of input patterns of the digits to acquire separate knowledge corresponding to each Road sign. In order to justify the effectiveness of the system, different test patterns of the signs are used to verify the system. Experimental results demonstrate that the system is capable of recognizing Road signs with 98% accuracy.

References

  • Haykin, S. , 2001. "Neural Networks: A Comprehensive Foundation", 2nd Edition, Pearson Education Asia, pp 13-23.
  • Mueller, R. , Steck, M. , 2003. "Road Sign Recognition", Term Paper, Computer Perception with Artificial Intelligence, University of Applied Sciences, Biel, Switzerland.
  • Piccioli, G. , De Micheli, E. , Parodi, P. , Campani, M. , 1996. "Robust Method for Road Sign Detection and Recognition", Image and Vision Computing 14, pp. 208-223.
  • Novovicova, J. , Paclik, P. , Pudil, P. , and Somol, P. , 2000. "Road Sign Classification Using Laplace Kernel Classifier," Pattern Recognition Letters 21, pp. 1165-1173.
  • Yuille, A. L. , Snow, D. , and Nitzberg,M. , 1998. "Using Color to Detect, Localize and Identify Informational Signs", Proc. International Conference on Computer Vision ICCV98, Bombay, India, pp. 628-633.
  • De la Escalera, A. , Moreno, L. , Salichs, M. A. , and Amingol, J. M. , 1997. "Road Traffic Sign Detection and Classification," IEEE Transactions Industrial Electronics, 44, pp. 848-859.
  • Lauziere, Y. , Gingras, D. , Ferrie, F. , 2001. "A Model-based Road Sign Identification System", Proc. IEEE Computer Conference on Computer Vision and Pattern Recognition, pp. 1163-1170.
  • Shape-based road sign detection and recognition for embedded application using MATLAB Md Sallah, S. S. ; Hussin, F. A. ; Yusoff, M. Z. ; Intelligent and Advanced Systems (ICIAS), 2010 Publication Year: 2010 , Page(s): 1 – 5
  • Fuzzy adaptive pre-processing models for road sign recognition. Chien-Chuan Lin; Ming-Shi Wang; Tang-Chun Yang; Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on Publication Year: 2010 , Page(s): 642 – 647
  • Michael Shneier, 2005. "Road Sign Detection and Recognition", IEEE Computer Society International Conference on Computer Vision and Pattern Recognition.
  • Sanjit Kumar Saha, Md. Shamsuzzaman, Prof. Dr. Md. Al-Amin Bhuiyan, 2010. "On Bangla Character Recognition", 13th International Conference on Computer and Information Technology, pp. 436-439.
  • Road sign detection and recognition system for real-time embedded applications. Sallah, S. S. M. ; Hussin, F. A. ; Yusoff, M. Z. ; Electrical, Control and Computer Engineering (INECCE), 2011 Publication Year: 2011, Page(s): 213 – 218
  • R. Ghica, S. Lu, and X. Yuan. Recogntion of traffic signs using a multilayer neural network. In Proc. Can Conf. On Electrical and Computer Engineering, 1994
  • Gonzalez, "Digital Image Processing", 3rd Edition, Pearson Education Asia, pp 336.