Road Surface Traffic Sign Detection with Hybrid Region Proposal and Fast R-CNN



Qian, Rongqiang, Liu, Qianyu, Yue, Yong, Coenen, Frans ORCID: 0000-0003-1026-6649 and Zhang, Bailing
(2016) Road Surface Traffic Sign Detection with Hybrid Region Proposal and Fast R-CNN. In: 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016-8-13 - 2016-8-15.

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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%.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Advanced Driver Assistance, traffic sign detection, deep learning, convolutional neural networks, Fast R-CNN
Depositing User: Symplectic Admin
Date Deposited: 22 Feb 2017 09:29
Last Modified: 19 Jan 2023 07:16
DOI: 10.1109/fskd.2016.7603233
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3005970