A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi

In recent years, research towards Autonomous Vehicle (AV) has grown up tremendously. The introduction of Advanced Driving Assistance System (ADAS) in AV has led researchers to explore on the key functionalities that an AV should have, proposes to create a convenience and safe environment where there...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohd Paudzi, Nurul Paudziah Aida
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Global Positioning System Real-time programming Programming languages (Electronic computers)
Online Access:	https://ir.uitm.edu.my/id/eprint/35625/1/35625.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.35625
record_format	uketd_dc
spelling	my-uitm-ir.356252020-11-27T04:04:44Z A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi 2020 Mohd Paudzi, Nurul Paudziah Aida Global Positioning System Real-time programming Programming languages (Electronic computers) In recent years, research towards Autonomous Vehicle (AV) has grown up tremendously. The introduction of Advanced Driving Assistance System (ADAS) in AV has led researchers to explore on the key functionalities that an AV should have, proposes to create a convenience and safe environment where there is no input acquire from a human throughout the journey. The key functionalities are including traffic sign recognition, parking space detection, pedestrian crash avoidance and blind spot detection. As the rises of road accident due to drivers’ carelessness and the distracted drivers that has been neglected the traffic signs on the road, this project aims to develop a traffic sign recognition system that can recognize in real-time for integrated on autonomous vehicles and to test the accuracy of the system. The image processing technique has been chosen to be applied on developing this system where the project is the implementation of the deep learning technology using You Only Look Once (YOLO) algorithm to train the model for detection. Five types of warning sign including crossroad, crossroad right, crossroad left, school children crossing and hump from Malaysian Traffic Sign is used. System testing was also conducted to study the accuracy of the detection and recognition towards traffic signs on the road. Test results reached real-time object detection with 96% accuracy on both traffic sign detection and recognition. The finding from this study is believed to be helpful as it may contribute to the automotive industry. In future, this system can be improved by integrating with the navigation system such as Global Positioning System (GPS) to make the system more functional and achieve more advance monitoring. 2020 Thesis https://ir.uitm.edu.my/id/eprint/35625/ https://ir.uitm.edu.my/id/eprint/35625/1/35625.pdf text en public degree Universiti Teknologi MARA, Cawangan Melaka Faculty of Computer and Mathematical Science Abu Mangshor, Nur Nabilah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Abu Mangshor, Nur Nabilah
topic	Global Positioning System Real-time programming Programming languages (Electronic computers)
spellingShingle	Global Positioning System Real-time programming Programming languages (Electronic computers) Mohd Paudzi, Nurul Paudziah Aida A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi
description	In recent years, research towards Autonomous Vehicle (AV) has grown up tremendously. The introduction of Advanced Driving Assistance System (ADAS) in AV has led researchers to explore on the key functionalities that an AV should have, proposes to create a convenience and safe environment where there is no input acquire from a human throughout the journey. The key functionalities are including traffic sign recognition, parking space detection, pedestrian crash avoidance and blind spot detection. As the rises of road accident due to drivers’ carelessness and the distracted drivers that has been neglected the traffic signs on the road, this project aims to develop a traffic sign recognition system that can recognize in real-time for integrated on autonomous vehicles and to test the accuracy of the system. The image processing technique has been chosen to be applied on developing this system where the project is the implementation of the deep learning technology using You Only Look Once (YOLO) algorithm to train the model for detection. Five types of warning sign including crossroad, crossroad right, crossroad left, school children crossing and hump from Malaysian Traffic Sign is used. System testing was also conducted to study the accuracy of the detection and recognition towards traffic signs on the road. Test results reached real-time object detection with 96% accuracy on both traffic sign detection and recognition. The finding from this study is believed to be helpful as it may contribute to the automotive industry. In future, this system can be improved by integrating with the navigation system such as Global Positioning System (GPS) to make the system more functional and achieve more advance monitoring.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Paudzi, Nurul Paudziah Aida
author_facet	Mohd Paudzi, Nurul Paudziah Aida
author_sort	Mohd Paudzi, Nurul Paudziah Aida
title	A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi
title_short	A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi
title_full	A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi
title_fullStr	A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi
title_full_unstemmed	A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi
title_sort	real-time traffic sign recognition system for autonomous vehicle using yolo / nurul paudziah aida mohd paudzi
granting_institution	Universiti Teknologi MARA, Cawangan Melaka
granting_department	Faculty of Computer and Mathematical Science
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/35625/1/35625.pdf
_version_	1783734309038850048

A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi

Similar Items