TY - GEN
T1 - Robust Chinese traffic sign detection and recognition with deep convolutional neural network
AU - Qian, Rongqiang
AU - Zhang, Bailing
AU - Yue, Yong
AU - Wang, Zhao
AU - Coenen, Frans
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/8
Y1 - 2016/1/8
N2 - Detection and recognition of traffic sign, including various road signs and text, play an important role in autonomous driving, mapping/navigation and traffic safety. In this paper, we proposed a traffic sign detection and recognition system by applying deep convolutional neural network (CNN), which demonstrates high performance with regard to detection rate and recognition accuracy. Compared with other published methods which are usually limited to a predefined set of traffic signs, our proposed system is more comprehensive as our target includes traffic signs, digits, English letters and Chinese characters. The system is based on a multi-task CNN trained to acquire effective features for the localization and classification of different traffic signs and texts. In addition to the public benchmarking datasets, the proposed approach has also been successfully evaluated on a field-captured Chinese traffic sign dataset, with performance confirming its robustness and suitability to real-world applications.
AB - Detection and recognition of traffic sign, including various road signs and text, play an important role in autonomous driving, mapping/navigation and traffic safety. In this paper, we proposed a traffic sign detection and recognition system by applying deep convolutional neural network (CNN), which demonstrates high performance with regard to detection rate and recognition accuracy. Compared with other published methods which are usually limited to a predefined set of traffic signs, our proposed system is more comprehensive as our target includes traffic signs, digits, English letters and Chinese characters. The system is based on a multi-task CNN trained to acquire effective features for the localization and classification of different traffic signs and texts. In addition to the public benchmarking datasets, the proposed approach has also been successfully evaluated on a field-captured Chinese traffic sign dataset, with performance confirming its robustness and suitability to real-world applications.
KW - component
KW - convolutional neural networks
KW - deep learning
KW - multi task CNN
KW - traffic sign detection
KW - traffic sign recognition
UR - https://www.scopus.com/pages/publications/84960430184
U2 - 10.1109/ICNC.2015.7378092
DO - 10.1109/ICNC.2015.7378092
M3 - Conference Proceeding
AN - SCOPUS:84960430184
T3 - Proceedings - International Conference on Natural Computation
SP - 791
EP - 796
BT - 2015 11th International Conference on Natural Computation, ICNC 2015
A2 - Xiao, Zheng
A2 - Tong, Zhao
A2 - Li, Kenli
A2 - Wang, Xingwei
A2 - Li, Keqin
PB - IEEE Computer Society
T2 - 11th International Conference on Natural Computation, ICNC 2015
Y2 - 15 August 2015 through 17 August 2015
ER -