A Modified PNN-based Method for Traffic Signs Classification
Zhang Hang · Systems Engineering · 2006
Neural networks with higher fault-tolerant and good adaptive learning ability find great applications in classification of traffic signs.But at the recent times,the existed methods such as multilayer perceptron,BP networks and RBF neural networks have also some essential drawbacks and deficiencies.So this paper gives a new method for classification of traffic signs based on modified probabilistic neural networks(PNN).Our classification algorithm is accomplished by two steps: the characters of traffic sign are extracted according to its Tchebichef moment invariants first,then based on modified PNN,traffic sign is identified.The simulation results validate the effectiveness of the proposed method.