Automatic road sign recognition using neural networks
Nguwi, Yok-Yen, and Kouzani, Abbas Z. (2006) Automatic road sign recognition using neural networks. Proceedings of the 2006 International Joint Conference of Neural Networks. 2006 International Joint Conference of Neural Networks (IJCNN 2006) , 16-21 July 2006, Vancouver, Canada , 7686 -7693.
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An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies road signs assisting the driver of the vehicle to properly operate the vehicle. This paper presents an automatic road sign recognition system capable of analysing live images, detecting multiple road signs within images, and classifying the type of the detected road signs. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space and locates road signs. The classification module determines the type of detected road signs using a series of one to one architectural Multi Layer Perceptron neural networks. The performances of the classifiers that are trained using Resillient Backpropagation and Scaled Conjugate Gradient algorithms are compared. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 96% using Scaled Conjugate Gradient trained classifiers.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Keywords:||backpropagation; conjugate gradient methods; image classification; image colour analysis; multilayer perceptrons|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 100%|
|Deposited On:||20 Sep 2012 12:31|
|Last Modified:||20 Sep 2012 18:03|
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