Title :
A Speed limit Sign Recognition System Using Artificial Neural Network
Author :
Ishak, Khairul Anuar ; Sani, Maizura Mohd ; Tahir, Nooritawati Md
Author_Institution :
Dept. of Electr., Electron. & Syst. Eng., Univ. Kebangsaan Malaysia, Bangi
Abstract :
This paper presents a real-time system to detect speed limit signs and remind drivers about the allowable speed limit in a specific road. The developed system consists of two main tasks, namely detection and recognition. In our work, speed limit sign is detected and extracted from real world scenes on the basis of their color and shape features. The detection task is based on a combination of color segmentation and shape detection techniques. It significantly speeds up the shape detection process by calculating the cross-correlation in frequency domain. Next, classification is then performed on extracted candidate region using multi-layer perceptron neural networks. Experiment results proved the feasibility of this system.
Keywords :
image colour analysis; image recognition; image segmentation; multilayer perceptrons; real-time systems; traffic engineering computing; artificial neural network; color segmentation; multilayer perceptron neural networks; real-time system; shape detection; speed limit sign recognition system; Artificial neural networks; Layout; Multi-layer neural network; Neural networks; Real time systems; Research and development; Retina; Roads; Shape; Systems engineering and theory; Artificial Neural Network; Color segmentation; Speed limit;
Conference_Titel :
Research and Development, 2006. SCOReD 2006. 4th Student Conference on
Conference_Location :
Selangor
Print_ISBN :
978-1-4244-0526-8
Electronic_ISBN :
978-1-4244-0527-5
DOI :
10.1109/SCORED.2006.4339324