Road surface traffic sign detection with hybrid region proposal and fast R-CNN

Abstract

Detection of traffic signs plays an important role in autonomous driving, traffic surveillance and traffic safety. Previous research in Traffic Sign Detection (TSD) generally focused on traffic signs which are over the roads, the traffic signs on road surface have not been discussed. In this paper, we propose a road surface traffic sign detection system by applying convolutional neural network (CNN). The proposed system consists of two main stages: 1) a hybrid region proposal method to hypothesize the traffic sign locations by taking into account complementary information of color and edge; 2) feature extraction, classification, bounding box regression and non-maximum suppression by Fast R-CNN. Extensive experiments have been conducted using our field-captured dataset, demonstrating outstanding performance with regard to high recall and precision rate. The overall average precision (AP) is about 85.58%.