Malaysia traffic sign recognition with convolutional neural network

Mian Mian Lau, King Hann Lim, Alpha Agape Gopalai

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

41 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Digital Signal Processing, DSP 2015
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781479980581, 9781479980581
Publication statusPublished - 9 Sept 2015
EventInternational Conference on Digital Signal Processing (DSP) 2015 - Singapore, Singapore
Duration: 21 Jul 201524 Jul 2015
Conference number: 20th (Proceedings)


ConferenceInternational Conference on Digital Signal Processing (DSP) 2015
Abbreviated titleDSP 2015
Internet address


  • Advance driver assistance system
  • Convolutional neural network
  • Radial basis function neural network
  • Traffic sign recognition

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