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...

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Main Author: Mohd Paudzi, Nurul Paudziah Aida
Format: Thesis
Language:English
Published: 2020
Subjects:
Online Access:http://ir.uitm.edu.my/id/eprint/35625/1/35625.pdf
http://ir.uitm.edu.my/id/eprint/35625/
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spelling my.uitm.ir.356252020-11-27T04:04:44Z http://ir.uitm.edu.my/id/eprint/35625/ A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi 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 NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/35625/1/35625.pdf Mohd Paudzi, Nurul Paudziah Aida (2020) A real-time traffic sign recognition system for autonomous vehicle using Yolo / Nurul Paudziah Aida Mohd Paudzi. Degree thesis, Universiti Teknologi MARA, Cawangan Melaka.
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
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
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
publishDate 2020
url http://ir.uitm.edu.my/id/eprint/35625/1/35625.pdf
http://ir.uitm.edu.my/id/eprint/35625/
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score 13.188452