Traffic signs detection and recognition by improved RBFNN

The paper develops radial basis function neural networks (RBFNN) applications in the traffic signs recognition. Firstly traffic signs are detected by using their color and shape informations. Then genetic algorithm (GA), which has a powerful global exploration capability, is applied to train RBFNN to obtain appropriate structures and parameters according to given objective functions. In order to improve recognition speed and accuracy, traffic signs are classified into three categories by special color and shape information. Three RBFNNs are designed for the three categories. Before fed into networks, the sign images are transformed into binary images and their features are optimized by linear discriminate analysis (LDA). The training set imitating possible sign transformations in real road conditions, is created to train and test the nets. The experimental results show the feasibility and validity of the proposed algorithm.