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Recognising Malaysia traffic sign with rain drop disturbance using deep learning

Traffic sign recognition is part of the driving assistance systems that can monitor driving by automatically recognising the traffic signs on the road and alerting drivers. In Malaysia, road accidents have remained one of the top principal causes of death in recent years. Other than the driver'...

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Bibliographic Details
Main Author:	Pang, Wan Chee
Format:	Final Year Project / Dissertation / Thesis
Published:	2022
Subjects:	H Social Sciences (General) HA Statistics Q Science (General) T Technology (General) TE Highway engineering. Roads and pavements
Online Access:	http://eprints.utar.edu.my/4939/1/fyp_2022_SC_PWC.pdf http://eprints.utar.edu.my/4939/
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id	my-utar-eprints.4939
record_format	eprints
spelling	my-utar-eprints.49392023-01-05T14:02:48Z Recognising Malaysia traffic sign with rain drop disturbance using deep learning Pang, Wan Chee H Social Sciences (General) HA Statistics Q Science (General) T Technology (General) TE Highway engineering. Roads and pavements Traffic sign recognition is part of the driving assistance systems that can monitor driving by automatically recognising the traffic signs on the road and alerting drivers. In Malaysia, road accidents have remained one of the top principal causes of death in recent years. Other than the driver's carelessness, weather such as rain might challenge the visibility and recognition of the traffic signs. Hence, we develop a road sign recognition system based on a deep learning algorithm that can identify the Malaysia traffic sign with raindrop disturbance. In this study, a convolutional neural network (CNN) is applied as it is a deep neural network that is well-known for effective image recognition. The algorithms are developed in Python language using Anaconda Spyder as the software. There are 18 different combinations of the epochs, dropout rates, and colour of the image input in the first convolutional layer (either colour or greyscale version) used in this study. In conclusion, we proposed an image recognition system with a predictive accuracy of 99.52% for Malaysia traffic signs with raindrop disturbance. 2022-05 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4939/1/fyp_2022_SC_PWC.pdf Pang, Wan Chee (2022) Recognising Malaysia traffic sign with rain drop disturbance using deep learning. Final Year Project, UTAR. http://eprints.utar.edu.my/4939/
institution	Universiti Tunku Abdul Rahman
building	UTAR Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tunku Abdul Rahman
content_source	UTAR Institutional Repository
url_provider	http://eprints.utar.edu.my
topic	H Social Sciences (General) HA Statistics Q Science (General) T Technology (General) TE Highway engineering. Roads and pavements
spellingShingle	H Social Sciences (General) HA Statistics Q Science (General) T Technology (General) TE Highway engineering. Roads and pavements Pang, Wan Chee Recognising Malaysia traffic sign with rain drop disturbance using deep learning
description	Traffic sign recognition is part of the driving assistance systems that can monitor driving by automatically recognising the traffic signs on the road and alerting drivers. In Malaysia, road accidents have remained one of the top principal causes of death in recent years. Other than the driver's carelessness, weather such as rain might challenge the visibility and recognition of the traffic signs. Hence, we develop a road sign recognition system based on a deep learning algorithm that can identify the Malaysia traffic sign with raindrop disturbance. In this study, a convolutional neural network (CNN) is applied as it is a deep neural network that is well-known for effective image recognition. The algorithms are developed in Python language using Anaconda Spyder as the software. There are 18 different combinations of the epochs, dropout rates, and colour of the image input in the first convolutional layer (either colour or greyscale version) used in this study. In conclusion, we proposed an image recognition system with a predictive accuracy of 99.52% for Malaysia traffic signs with raindrop disturbance.
format	Final Year Project / Dissertation / Thesis
author	Pang, Wan Chee
author_facet	Pang, Wan Chee
author_sort	Pang, Wan Chee
title	Recognising Malaysia traffic sign with rain drop disturbance using deep learning
title_short	Recognising Malaysia traffic sign with rain drop disturbance using deep learning
title_full	Recognising Malaysia traffic sign with rain drop disturbance using deep learning
title_fullStr	Recognising Malaysia traffic sign with rain drop disturbance using deep learning
title_full_unstemmed	Recognising Malaysia traffic sign with rain drop disturbance using deep learning
title_sort	recognising malaysia traffic sign with rain drop disturbance using deep learning
publishDate	2022
url	http://eprints.utar.edu.my/4939/1/fyp_2022_SC_PWC.pdf http://eprints.utar.edu.my/4939/
_version_	1754534175160926208
score	13.211869

Recognising Malaysia traffic sign with rain drop disturbance using deep learning

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