NDLI: Malaysia traffic sign recognition with convolutional neural network

Content Provider	IEEE Xplore Digital Library
Author	Mian Mian Lau King Hann Lim Gopalai, A.A.
Copyright Year	2015
Description	Author affiliation: Curtin Sarawak Res. Inst., Curtin Univ., Miri, Malaysia (Mian Mian Lau; King Hann Lim) \|\| Sch. of Eng., Monash Univ. Malaysia, Bandar Sunway, Malaysia (Gopalai, A.A.)
Abstract	Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various kind of scenarios, even those which were not used during training. Therefore, a deep architecture neural network is implemented to perform traffic sign classification in order to improve the traffic sign recognition rate. A comparative study for a deep and shallow architecture neural network is presented in this paper. Deep and shallow architecture neural network refer to convolutional neural network (CNN) and radial basis function neural network (RBFNN) respectively. In the simulation result, two types of training modes had been compared i.e. incremental training and batch training. Experimental results show that incremental training mode trains faster than batch training mode. The performance of the convolutional neural network is evaluated with the Malaysian traffic sign database and achieves 99% of the recognition rate.
Starting Page	1006
Ending Page	1010
File Size	877898
Page Count	5
File Format	PDF
ISSN	21653577
e-ISBN	9781479980581
DOI	10.1109/ICDSP.2015.7252029
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-21
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Computer architecture Biological neural networks Vehicles Testing Roads Convolutional neural network Advance driver assistance system Traffic sign recognition Radial basis function neural network
Content Type	Text
Resource Type	Article

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