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

Rongqiang Qian, Qianyu Liu, Yong Yue, Frans Coenen, Bailing Zhang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

42 Citations (Scopus)

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%.

Original languageEnglish
Title of host publication2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
EditorsJiayi Du, Chubo Liu, Kenli Li, Lipo Wang, Zhao Tong, Maozhen Li, Ning Xiong
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages555-559
Number of pages5
ISBN (Electronic)9781509040933
DOIs
Publication statusPublished - 19 Oct 2016
Event12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016 - Changsha, China
Duration: 13 Aug 201615 Aug 2016

Publication series

Name2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016

Conference

Conference12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
Country/TerritoryChina
CityChangsha
Period13/08/1615/08/16

Keywords

  • Advanced Driver Assistance
  • Fast R-CNN
  • convolutional neural networks
  • deep learning
  • traffic sign detection

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