TY - GEN
T1 - Road surface traffic sign detection with hybrid region proposal and fast R-CNN
AU - Qian, Rongqiang
AU - Liu, Qianyu
AU - Yue, Yong
AU - Coenen, Frans
AU - Zhang, Bailing
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/19
Y1 - 2016/10/19
N2 - 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%.
AB - 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%.
KW - Advanced Driver Assistance
KW - Fast R-CNN
KW - convolutional neural networks
KW - deep learning
KW - traffic sign detection
UR - https://www.scopus.com/pages/publications/84997824433
U2 - 10.1109/FSKD.2016.7603233
DO - 10.1109/FSKD.2016.7603233
M3 - Conference Proceeding
AN - SCOPUS:84997824433
T3 - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
SP - 555
EP - 559
BT - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
A2 - Du, Jiayi
A2 - Liu, Chubo
A2 - Li, Kenli
A2 - Wang, Lipo
A2 - Tong, Zhao
A2 - Li, Maozhen
A2 - Xiong, Ning
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
Y2 - 13 August 2016 through 15 August 2016
ER -