Citation

Zhu Y, Zhang C, Zhou D, Wang X, Bai X, Liu W. Neurocomputing 2016; 214: 758-766.

Copyright

(Copyright © 2016, Elsevier Publishing)

DOI

10.1016/j.neucom.2016.07.009

PMID

unavailable

Abstract

Detecting and recognizing traffic signs is a hot topic in the field of computer vision with lots of applications, e.g., safe driving, path planning, robot navigation etc. We propose a novel framework with two deep learning components including fully convolutional network (FCN) guided traffic sign proposals and deep convolutional neural network (CNN) for object classification. Our core idea is to use CNN to classify traffic sign proposals to perform fast and accurate traffic sign detection and recognition. Due to the complexity of the traffic scene, we improve the state-of-the-art object proposal method, EdgeBox, by incorporating with a trained FCN. The FCN guided object proposals can produce more discriminative candidates, which help to make the whole detection system fast and accurate. In the experiments, we have evaluated the proposed method on publicly available traffic sign benchmark, Swedish Traffic Signs Dataset (STSD), and achieved the state-of-the-art results.

Language: en