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    Malaysia traffic sign recognition with convolutional neural network

    Access Status
    Fulltext not available
    Authors
    Lau, M.
    Lim, Hann
    Gopalai, A.
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Lau, M. and Lim, H. and Gopalai, A. 2015. Malaysia traffic sign recognition with convolutional neural network, in Proccedings of the International Conference on Digital Signal Processing, Jul 21-24 2015, pp. 1006-1010. Singapore: IEEE.
    Source Title
    International Conference on Digital Signal Processing, DSP
    DOI
    10.1109/ICDSP.2015.7252029
    ISBN
    9781479980581
    School
    Curtin Sarawak
    URI
    http://hdl.handle.net/20.500.11937/46509
    Collection
    • Curtin Research Publications
    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.

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