Staff View: Investigation Of Traffic Sign Image Classification For Self Driving Car

Investigation Of Traffic Sign Image Classification For Self Driving Car

The way we live has changed as a result of technological advancements. Artificial Intelligence has had a good impact on all fields and is making our lives easier. The development of a technology-assisted traffic system is a significant step forward in the automotive industry. With the growth of auto...

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Main Author:	Farra Herliena, Md Zin
Format:	Undergraduates Project Papers
Language:	English
Published:	2022
Subjects:	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/39895/1/EA18085_FARRA%20HERLIENA_THESIS%20%20-%20FARRA%20HERLIENA.pdf http://umpir.ump.edu.my/id/eprint/39895/
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spelling	my.ump.umpir.398952024-01-08T06:54:07Z http://umpir.ump.edu.my/id/eprint/39895/ Investigation Of Traffic Sign Image Classification For Self Driving Car Farra Herliena, Md Zin TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering The way we live has changed as a result of technological advancements. Artificial Intelligence has had a good impact on all fields and is making our lives easier. The development of a technology-assisted traffic system is a significant step forward in the automotive industry. 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, color 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 the accident and enhance the credibility of self-driving 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. A CNN model for deep nature is suggested in this paper. The CNN Model for Traffic Signs is consists of four dense layers or layers that are entirely linked. Because it learns characteristics from all of the attributes of the preceding layers, a fully connected layer learns deeply. 2022-06 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39895/1/EA18085_FARRA%20HERLIENA_THESIS%20%20-%20FARRA%20HERLIENA.pdf Farra Herliena, Md Zin (2022) Investigation Of Traffic Sign Image Classification For Self Driving Car. College of Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah.
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
building	UMPSA Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Pahang Al-Sultan Abdullah
content_source	UMPSA Institutional Repository
url_provider	http://umpir.ump.edu.my/
language	English
topic	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Farra Herliena, Md Zin Investigation Of Traffic Sign Image Classification For Self Driving Car
description	The way we live has changed as a result of technological advancements. Artificial Intelligence has had a good impact on all fields and is making our lives easier. The development of a technology-assisted traffic system is a significant step forward in the automotive industry. 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, color 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 the accident and enhance the credibility of self-driving 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. A CNN model for deep nature is suggested in this paper. The CNN Model for Traffic Signs is consists of four dense layers or layers that are entirely linked. Because it learns characteristics from all of the attributes of the preceding layers, a fully connected layer learns deeply.
format	Undergraduates Project Papers
author	Farra Herliena, Md Zin
author_facet	Farra Herliena, Md Zin
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/39895/1/EA18085_FARRA%20HERLIENA_THESIS%20%20-%20FARRA%20HERLIENA.pdf http://umpir.ump.edu.my/id/eprint/39895/
_version_	1822924035404595200
score	13.232414

Investigation Of Traffic Sign Image Classification For Self Driving Car

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