Intelligence approach of traffic sign recognition based on color standardization

Nowadays, for the BP neural network based outdoor traffic sign recognition problems, the recognition rate is generally between 60% and 70%. Based on the results analysis, one may come to a conclusion that the key factors affecting recognition rate are the color distortion caused by the color complexity. This paper present a new solution according to the idea of simplifying the complex problem, using color information and intelligent approach. The first step is to break the complex color information down to 5 kinds of standard color, and then employ BP neural network to classification. In this article BP network is used for color standardization, selecting 23 normalization signs as training set and 531 real signs as testing set for BP network. By doing so 100% average recognition rate is achieved. At the same time, it shows the better robustness of the proposed approach for the color distortion of traffic sign in terms of either the structure parameter or the training parameter of network.