On Recognition for Traffic Signs Based on Neural Network
XU Zhong-ren · 2003
The basic theory of a neural network classifier was introduced. For three kinds of traffic signs,such as indicative signs,warning signs and prohibitive signs, a method based neural network classifier was developed to recognize traffic signs. The classifier consists of two network layers. The front network is made up of a BP networks and will do a rough classification task. The back network consists of three BP networks and will fine the classification done before. The whole process completes the recognition task. Compared with a conventional single layer classifier,the training speed and the ratio of recognition of this classifier are greatly increased. It accords with the fact that the neural network has higher correctness and fast training speed when it solves small-scale problems. At the same time,when additional training samples are added, only the corresponding network needs traineing again but the whole network. The experimental results show that the automatic recognition method based on neural network has good effect.