Traffic sign classification based on neural network for advance driver assistance system

Mahammad A. Hannan, Safat B. Wali, Tan J. Pin, Aini Hussain, Salina A. Samad

Research output: Contribution to journalArticle

2 Citations (Scopus)


© 2014, Wydawnictwo SIGMA - N O T Sp. z o.o. All rights reserved. Traffic sign is utmost important information or rule in transportation. In order to ensure the transportation safety the automotive industry has developed Advance Driver Assistance System (ADAS). Among the ADAS system, development of TSDR is the most challenging to the researchers and developers due to unsatisfying performance. This paper deals with, automatic traffic sign classification and reduces the effect of illumination and variable lighting over the classification scheme by using neural network according to the traffic sign shape. There are three main phase of the classification scheme such as; pre-processing using image normalization, feature extraction using color information of 16-point pixel values and multilayer feed forward neural network for classification. An accuracy rate of 84.4% has been achieved by the proposed system. Overall processing time of 0.134s shows the system is a fast system and real-time application.
Original languageEnglish
Pages (from-to)169-172
Number of pages151
JournalPrzeglad Elektrotechniczny
Publication statusPublished - 01 Jan 2014
Externally publishedYes


All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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