Traffic sign recognition by color segmentation and neural network

Thongchai Surinwarangkoon; Supot Nitsuwat; Elvin J. Moore

doi:10.1117/12.920243

13 January 2012 Traffic sign recognition by color segmentation and neural network

Thongchai Surinwarangkoon, Supot Nitsuwat, Elvin J. Moore

Proceedings Volume 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies; 83501K (2012) https://doi.org/10.1117/12.920243
Event: Fourth International Conference on Machine Vision (ICMV 11), 2011, Singapore, Singapore

Abstract

An algorithm is proposed for traffic sign detection and identification based on color filtering, color segmentation and neural networks. Traffic signs in Thailand are classified by color into four types: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). A color filtering method is first used to detect traffic signs and classify them by type. Then color segmentation methods adapted for each color type are used to extract inner features, e.g., arrows, bars etc. Finally, neural networks trained to recognize signs in each color type are used to identify any given traffic sign. Experiments show that the algorithm can improve the accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.

Citation Download Citation

Thongchai Surinwarangkoon, Supot Nitsuwat, and Elvin J. Moore "Traffic sign recognition by color segmentation and neural network", Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83501K (13 January 2012); https://doi.org/10.1117/12.920243

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