Real-Time Traffic Sign Classification Using Combined Convolutional Neural Networks

Abstract

The traffic sign recognition system inside the vehicle plays an important role and could guarantee the safety of human life on the road since it feedbacks road information to the driver in time. Benefited from learning features of the traffic sign, the convolutional neural network (CNN) has been widely used in traffic sign recognition with a high accuracy. However, the different kinds of traffic signs appear to distinctive features. A deep and high complexity neural network with a larger number of parameters is usually required to adapt the distinctive features, while it tends to be time-consuming and can not meet real-time requirement. In this paper, we firstly divide traffic signs into hierarchal structure according to the types of features, and then use a combined CNNs (CCNN) to adapt the hierarchical traffic signs, where the probabilities of superclass and subclass the sign belongs to are calculated using two CNNs with a simple network. Finally, classifying of the sign can be achieved by the weighted output of the two CNNs, and a low complexity sign recognition system could be obtained. Simulation results on the GTSRB database show that the proposed method achieves comparable accuracy and less time-consuming to the state-of-the-art methods.