Design and Optimization of the Model for Traffic Signs Classification Based on Convolutional Neural Networks

Abstract

Recently, convolutional neural networks (CNNs) demonstrate state-of-the-art performance in computer vision such as classification, recognition and detection. In this paper, a traffic signs classification system based on CNNs is proposed. Generally, a convolutional network usually has a large number of parameters which need millions of data and a great deal of time to train. To solve this problem, the strategy of transfer learning is utilized in this paper. Besides, further improvement is implemented on the chosen model to improve the performance of the network by changing some fully connected layers into convolutional connection. This is because that the weight shared feature of convolutional layers is able to reduce the number of parameters contained in a network. In addition, these convolutional kernels are decomposed into multi-layer and smaller convolutional kernels to get a better performance. Finally, the performance of the final optimized network is compared with unoptimized networks. Experimental results demonstrate that the final optimized network presents the best performance.