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Investigation of traffic sign image classification for self driving car

Artificial Intelligence has had a good impact on all fields and is making our lives easier. With the growth of autonomous vehicles, the automotive industry is improving rapidly. Autonomous vehicles are a certain conclusion in the future, and they are intended to be both safe and convenient. One of t...

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Main Authors:	Farra Herliena, Md Zin, Fahmi, Samsuri
Format:	Conference or Workshop Item
Language:	English
Published:	2022
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/37113/1/Investigation%20of%20traffic%20sign%20image%20classification%20for%20self%20driving%20car.pdf http://umpir.ump.edu.my/id/eprint/37113/ https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files
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id	my.ump.umpir.37113
record_format	eprints
spelling	my.ump.umpir.371132023-03-14T06:59:44Z http://umpir.ump.edu.my/id/eprint/37113/ Investigation of traffic sign image classification for self driving car Farra Herliena, Md Zin Fahmi, Samsuri T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Artificial Intelligence has had a good impact on all fields and is making our lives easier. With the growth of autonomous vehicles, the automotive industry is improving rapidly. Autonomous vehicles are a certain conclusion in the future, and they are intended to be both safe and convenient. One of the most critical issues for autonomous vehicles is traffic sign classification. Half occlusion, colour fade by surrounding barriers, variations in shadows, reflections on signboards during the day, and movement blurring different lighting and weather situations are some of the most typical issues that might occur when identifying and detecting traffic signs. In the classification and identification of road signs, the performance of a Convolutional Neural Network (CNN) has outperformed the same of humans. The purpose of this study is to boost the accuracy of this classification in order to minimize accidents and enhance the credibility of selfdriving vehicles. Otherwise, the ecology of traffic may be jeopardised. Using image processing and machine vision processing technologies, as well as the use of in-depth learning in target classification, the traffic sign recognition method based on CNN is studied. A traffic sign detection and classification method with high efficiency and high efficiency are proposed. The German Traffic Sign Recognition Benchmark (GTSRB) is employed to test the approach method, and the results reveal that it outperforms state-of-the-art approaches. 2022-11-15 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37113/1/Investigation%20of%20traffic%20sign%20image%20classification%20for%20self%20driving%20car.pdf Farra Herliena, Md Zin and Fahmi, Samsuri (2022) Investigation of traffic sign image classification for self driving car. In: The 6th National Conference for Postgraduate Research (NCON-PGR 2022), 15 November 2022 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. p. 158.. https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files
institution	Universiti Malaysia Pahang
building	UMP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang
content_source	UMP Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Farra Herliena, Md Zin Fahmi, Samsuri Investigation of traffic sign image classification for self driving car
description	Artificial Intelligence has had a good impact on all fields and is making our lives easier. With the growth of autonomous vehicles, the automotive industry is improving rapidly. Autonomous vehicles are a certain conclusion in the future, and they are intended to be both safe and convenient. One of the most critical issues for autonomous vehicles is traffic sign classification. Half occlusion, colour fade by surrounding barriers, variations in shadows, reflections on signboards during the day, and movement blurring different lighting and weather situations are some of the most typical issues that might occur when identifying and detecting traffic signs. In the classification and identification of road signs, the performance of a Convolutional Neural Network (CNN) has outperformed the same of humans. The purpose of this study is to boost the accuracy of this classification in order to minimize accidents and enhance the credibility of selfdriving vehicles. Otherwise, the ecology of traffic may be jeopardised. Using image processing and machine vision processing technologies, as well as the use of in-depth learning in target classification, the traffic sign recognition method based on CNN is studied. A traffic sign detection and classification method with high efficiency and high efficiency are proposed. The German Traffic Sign Recognition Benchmark (GTSRB) is employed to test the approach method, and the results reveal that it outperforms state-of-the-art approaches.
format	Conference or Workshop Item
author	Farra Herliena, Md Zin Fahmi, Samsuri
author_facet	Farra Herliena, Md Zin Fahmi, Samsuri
author_sort	Farra Herliena, Md Zin
title	Investigation of traffic sign image classification for self driving car
title_short	Investigation of traffic sign image classification for self driving car
title_full	Investigation of traffic sign image classification for self driving car
title_fullStr	Investigation of traffic sign image classification for self driving car
title_full_unstemmed	Investigation of traffic sign image classification for self driving car
title_sort	investigation of traffic sign image classification for self driving car
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/37113/1/Investigation%20of%20traffic%20sign%20image%20classification%20for%20self%20driving%20car.pdf http://umpir.ump.edu.my/id/eprint/37113/ https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files
_version_	1761616606864080896
score	13.211869

Investigation of traffic sign image classification for self driving car

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