Title: NEURAL NETWORK BASED AUTOMATIC TRAFFIC SIGNS RECOGNITION

Issue Number: Vol. 1, No. 4
Year of Publication: 2011
Page Numbers: 753-766
Authors: Mohammad A. N. Al-Azawi
Journal Name: International Journal of Digital Information and Wireless Communications (IJDIWC)
- Hong Kong

Abstract:


Image recognition and understanding is one of the most interesting fields of researches. Its main idea is to bridge the gap between the high level human image understanding and the low level machine image representation. Quite a lot of applications have been suggested in different fields like medicine, industry, robotics, satellite imagery and other applications. This paper proposes a new approach of traffic signs image recognition and understanding using computational intelligent techniques and the application of this approach on intelligent cars which can recognize the traffic signs and take a decision according to the signs it reads. Supervised machine learning has been selected since the algorithm does not need to classify the images but to identify their precise meaning. Different neural networks have been trained and used in this paper. The best neural network has been selected, which uses genetic algorithms in its training, and is known as evolutionary training neural network. Different image features have also been investigated and discussed. the best of these features, which fit the requirement of the suggested algorithm, have been selected.