Authors: Huiming Dai; Xin Zhang; Dacheng Yang
Addresses: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China ' School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China ' School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China
Abstract: As road traffic sign recognition is a crucial component for automatic driver assistance systems, it is a key problem in computer vision as well. Therefore, in this paper, we study on the problem of road traffic sign recognition utilising the computer vision technology. The main innovation of this paper is to propose an improved convolutional neural network, and then use it to tackle the road traffic sign recognition problem. Convolutional neural network can learn features from training data set, and a convolutional network contains alternating layers of convolution and pooling. Particularly, RGB traffic images are transformed to grey scale images, and then grey scale images are input to the improved convolutional neural network. Furthermore, the fixed layers are utilised to discover region of interests, and the learnable layers are used to extract features. In general, output information of the proposed two learnable layers are input to the classifier separately, and parameters of learnable layers and the classifier are trained at the same time. Finally, GTSDB data set is chosen to make performance evaluation, among which 600 images and 300 images are regarded as training and testing data set respectively. Experimental results demonstrate that the improved CNN-based traffic sign recognition performs better than the traditional CNN.
Keywords: road traffic sign; object recognition; computer vision; convolutional neural network.
International Journal of Computational Vision and Robotics, 2018 Vol.8 No.1, pp.85 - 93
Received: 21 Dec 2016
Accepted: 17 Jan 2017
Published online: 27 Feb 2018 *