Rozpoznawanie i klasyfikacja znaków drogowych z wykorzystaniem sieci neuronowych

Translated title of the contribution: Traffic sign classification based on neural network for advance driver assistance system

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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 languageUndefined/Unknown
Pages (from-to)169-172
Number of pages4
JournalPrzeglad Elektrotechniczny
Volume90
DOIs
Publication statusPublished - 1 Nov 2014

Fingerprint

Traffic signs
Neural networks
Lighting
Feedforward neural networks
Multilayer neural networks
Automotive industry
Feature extraction
Image processing
Pixels
Color
Processing

Keywords

  • Advance driver assistance system
  • Image normalization
  • Neural network
  • Traffic sign classification

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Rozpoznawanie i klasyfikacja znaków drogowych z wykorzystaniem sieci neuronowych. / M A, Hannan; Wali, Safat B.; Pin, Tan J.; Hussain, Aini; Abdul Samad, Salina.

In: Przeglad Elektrotechniczny, Vol. 90, 01.11.2014, p. 169-172.

Research output: Contribution to journalArticle

@article{2867b829aa9b4d258f522b88d12aa7f0,
title = "Rozpoznawanie i klasyfikacja znak{\'o}w drogowych z wykorzystaniem sieci neuronowych",
abstract = "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.",
keywords = "Advance driver assistance system, Image normalization, Neural network, Traffic sign classification",
author = "{M A}, Hannan and Wali, {Safat B.} and Pin, {Tan J.} and Aini Hussain and {Abdul Samad}, Salina",
year = "2014",
month = "11",
day = "1",
doi = "10.12915/pe.2014.11.44",
language = "Undefined/Unknown",
volume = "90",
pages = "169--172",
journal = "Przeglad Elektrotechniczny",
issn = "0033-2097",
publisher = "Wydawnictwo SIGMA - N O T Sp. z o.o.",

}

TY - JOUR

T1 - Rozpoznawanie i klasyfikacja znaków drogowych z wykorzystaniem sieci neuronowych

AU - M A, Hannan

AU - Wali, Safat B.

AU - Pin, Tan J.

AU - Hussain, Aini

AU - Abdul Samad, Salina

PY - 2014/11/1

Y1 - 2014/11/1

N2 - 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.

AB - 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.

KW - Advance driver assistance system

KW - Image normalization

KW - Neural network

KW - Traffic sign classification

UR - http://www.scopus.com/inward/record.url?scp=84908555116&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908555116&partnerID=8YFLogxK

U2 - 10.12915/pe.2014.11.44

DO - 10.12915/pe.2014.11.44

M3 - Article

VL - 90

SP - 169

EP - 172

JO - Przeglad Elektrotechniczny

JF - Przeglad Elektrotechniczny

SN - 0033-2097

ER -