Deep learning–based traffic sign recognition for unmanned autonomous vehicles. (May 2018)

• Record Type: Journal Article
• Authors: Zang, Di; Wei, Zhihua; Bao, Maomao; Cheng, Jiujun; Zhang, Dongdong; Tang, Keshuang; Li, Xin
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 232:Number 5(2018)
• Page Start: 497
• Page End: 505
• Publication Date: 2018-05
• Subjects: Traffic sign recognition -- quaternion convolutional neural network -- unmanned autonomous vehicle; Mechanical engineering -- Periodicals; Automatic control -- Periodicals; Systems engineering -- Periodicals; 621.3
• DOI: 10.1177/0959651818758865
• Languages: English
• ISSNs: 0959-6518

Abstract

Being one of the key techniques for unmanned autonomous vehicle, traffic sign recognition is applied to assist autopilot. Colors are very important clues to identify traffic signs; however, color-based methods suffer performance degradation in the case of light variation. Convolutional neural network, as one of the deep learning methods, is able to hierarchically learn high-level features from the raw input. It has been proved that convolutional neural network–based approaches outperform the color-based ones. At present, inputs of convolutional neural networks are processed either as gray images or as three independent color channels; the learned color features are still not enough to represent traffic signs. Apart from colors, temporal constraint is also crucial to recognize video-based traffic signs. The characteristics of traffic signs in the time domain require further exploration. Quaternion numbers are able to encode multi-dimensional information, and they have been employed to describe color images. In this article, we are inspired to present a quaternion convolutional neural network–based approach to recognize traffic signs by fusing spatial and temporal features in a single framework. Experimental results illustrate that the proposed method can yield correct recognition results and obtain better performance when compared with the state-of-the-art work.