Traffic Sign Recognition and Classification Using Deep Convolution Neural Network

Abstract

Traffic signs classification is that the process of identifying which class a traffic sign belongs to. There are several differing kinds of traffic signs like speed limits, no entry, turn left or right, etc. A neural network model based on deep learning is utilized to explore the traffic sign recognition (TSR) and expand the application of deep intelligent learning technology in the field of virtual reality (VR) image recognition, thereby assessing the road traffic safety risks and promoting the construction of intelligent transportation networks Firstly, a dual-path deep CNN (TDCNN) TSR model is constructed supported CNN, and recognition accuracy is calculated to research the training results of the model. Secondly, we assess the road traffic safety risks, and also the prediction and evaluation effects. Finally, the changes in safety risks of road traffic accidents are analyzed based on the two key influencing factors of the number of road intersections and the speed of vehicles traveling. The results show that the learning rate of the network model and the number of hidden neurons in the fully-connected layer directly affect the training results, and there are differences in the choices between the early and late training periods. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. Key Words: Deep Convolutional Neural Network CNN, TensorFlow, Image Processing, ReLU Activation Function.