Abstract:
Both unmanned vehicles and driver assistance systems require solving the problem of traffic sign recognition. A lot of work has been done in this area, but no approach ha...Show MoreMetadata
Abstract:
Both unmanned vehicles and driver assistance systems require solving the problem of traffic sign recognition. A lot of work has been done in this area, but no approach has been presented to perform the task with high accuracy and high speed under various conditions until now. In this paper, we have designed and implemented a detector by adopting the framework of faster R-convolutional neural networks (CNN) and the structure of MobileNet. Here, color and shape information have been used to refine the localizations of small traffic signs, which are not easy to regress precisely. Finally, an efficient CNN with asymmetric kernels is used to be the classifier of traffic signs. Both the detector and the classifier have been trained on challenging public benchmarks. The results show that the proposed detector can detect all categories of traffic signs. The detector and the classifier proposed here are proved to be superior to the state-of-the-art method. Our code and results are available online.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 20, Issue: 3, March 2019)
Funding Agency:

Department of Automation, University of Science and Technology of China, Hefei, China
Jia Li was born in 1993. He received the B.S. degree in automation from Hefei University of Technology, Hefei, China, in 2016. He is currently pursuing the master’s degree with the Department of Automation, University of Science and Technology of China. His research interests are in computer vision and deep learning.
Jia Li was born in 1993. He received the B.S. degree in automation from Hefei University of Technology, Hefei, China, in 2016. He is currently pursuing the master’s degree with the Department of Automation, University of Science and Technology of China. His research interests are in computer vision and deep learning.View more

Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China
Zengfu Wang (M’13) received the B.S. degree in electronic engineering from University of Science and Technology of China in 1982 and the Ph.D. degree in control engineering from Osaka University, Japan, in 1992. He is currently a Professor with the Institute of Intelligent Machines, Chinese Academy of Sciences, and the University of Science and Technology of China. He has authored over 200 journal articles and conference ...Show More
Zengfu Wang (M’13) received the B.S. degree in electronic engineering from University of Science and Technology of China in 1982 and the Ph.D. degree in control engineering from Osaka University, Japan, in 1992. He is currently a Professor with the Institute of Intelligent Machines, Chinese Academy of Sciences, and the University of Science and Technology of China. He has authored over 200 journal articles and conference ...View more

Department of Automation, University of Science and Technology of China, Hefei, China
Jia Li was born in 1993. He received the B.S. degree in automation from Hefei University of Technology, Hefei, China, in 2016. He is currently pursuing the master’s degree with the Department of Automation, University of Science and Technology of China. His research interests are in computer vision and deep learning.
Jia Li was born in 1993. He received the B.S. degree in automation from Hefei University of Technology, Hefei, China, in 2016. He is currently pursuing the master’s degree with the Department of Automation, University of Science and Technology of China. His research interests are in computer vision and deep learning.View more

Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China
Zengfu Wang (M’13) received the B.S. degree in electronic engineering from University of Science and Technology of China in 1982 and the Ph.D. degree in control engineering from Osaka University, Japan, in 1992. He is currently a Professor with the Institute of Intelligent Machines, Chinese Academy of Sciences, and the University of Science and Technology of China. He has authored over 200 journal articles and conference papers. His research interests include computer vision, human–computer interaction, and intelligent robots. He received the Best Paper Award from the ACM International Conference on Multimedia in 2009.
Zengfu Wang (M’13) received the B.S. degree in electronic engineering from University of Science and Technology of China in 1982 and the Ph.D. degree in control engineering from Osaka University, Japan, in 1992. He is currently a Professor with the Institute of Intelligent Machines, Chinese Academy of Sciences, and the University of Science and Technology of China. He has authored over 200 journal articles and conference papers. His research interests include computer vision, human–computer interaction, and intelligent robots. He received the Best Paper Award from the ACM International Conference on Multimedia in 2009.View more