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Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild

文献类型：期刊论文

作者Li, Jia1; Wang, Zengfu2,3
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2019-03-01
卷号20期号:3页码:975-984
关键词Traffic sign recognition Faster R-CNN localization refinement efficient CNN
ISSN号1524-9050
DOI10.1109/TITS.2018.2843815
通讯作者Wang, Zengfu(zfwang@ustc.edu.cn)
英文摘要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.
WOS关键词ALGORITHMS
资助项目National Natural Science Foundation of China[61472393]
WOS研究方向Engineering ; Transportation
语种英语
WOS记录号WOS:000460758300015
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/42372]  
专题合肥物质科学研究院_中科院固体物理研究所
通讯作者Wang, Zengfu
作者单位1.Univ Sci & Technol China, Dept Automat, Hefei 230026, Anhui, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
3.Natl Engn Lab Speech & Language Informat Proc, Hefei 230026, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Li, Jia,Wang, Zengfu. Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2019,20(3):975-984.
APA Li, Jia,&Wang, Zengfu.(2019).Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,20(3),975-984.
MLA Li, Jia,et al."Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 20.3(2019):975-984.

入库方式： OAI收割

来源：合肥物质科学研究院

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