Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]

This study evaluates the performance of the YOLOv5 model in the detection of traffic signs under a diverse range of environmental conditions, assessing its performance through a comprehensive set of experiments. This study assesses the model's precision in identifying signage categories across...

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Main Authors: Anaz Anizan, Nur Izzati, Ahmat Ruslan, Fazlina, Abd Razak, Noorfadzli, Abdul Aziz, Mohd Azri, Johari, Juliana
Format: Article
Language:English
Published: UiTM Press 2024
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Online Access:https://ir.uitm.edu.my/id/eprint/105787/1/105787.pdf
https://ir.uitm.edu.my/id/eprint/105787/
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spelling my.uitm.ir.1057872024-11-07T02:46:33Z https://ir.uitm.edu.my/id/eprint/105787/ Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.] jeesr Anaz Anizan, Nur Izzati Ahmat Ruslan, Fazlina Abd Razak, Noorfadzli Abdul Aziz, Mohd Azri Johari, Juliana Machine learning Safety and traffic control devices This study evaluates the performance of the YOLOv5 model in the detection of traffic signs under a diverse range of environmental conditions, assessing its performance through a comprehensive set of experiments. This study assesses the model's precision in identifying signage categories across a variety of lighting conditions and perspectives by employing a robust dataset that includes 1,596 images of a wide range of traffic signs. The model's ability to maintain high detection accuracy in optimal conditions is the primary focus of the analysis, which also emphasizes the challenges encountered in adverse lighting conditions such as direct sunlight and low-light settings in parking lots. The results indicate that YOLOv5 is highly reliable in unobstructed and clear conditions, but its reliability decreases in complex environments. This paper examines potential enhancements and future research directions, such as exploring of alternative model architectures and the implementation of advanced data augmentation techniques, to improve the adaptability and robustness of traffic sign detection systems. UiTM Press 2024-10 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/105787/1/105787.pdf Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]. (2024) Journal of Electrical and Electronic Systems Research (JEESR) <https://ir.uitm.edu.my/view/publication/Journal_of_Electrical_and_Electronic_Systems_Research_=28JEESR=29/>, 25 (1): 11. pp. 99-107. ISSN 1985-5389
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 Machine learning
Safety and traffic control devices
spellingShingle Machine learning
Safety and traffic control devices
Anaz Anizan, Nur Izzati
Ahmat Ruslan, Fazlina
Abd Razak, Noorfadzli
Abdul Aziz, Mohd Azri
Johari, Juliana
Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]
description This study evaluates the performance of the YOLOv5 model in the detection of traffic signs under a diverse range of environmental conditions, assessing its performance through a comprehensive set of experiments. This study assesses the model's precision in identifying signage categories across a variety of lighting conditions and perspectives by employing a robust dataset that includes 1,596 images of a wide range of traffic signs. The model's ability to maintain high detection accuracy in optimal conditions is the primary focus of the analysis, which also emphasizes the challenges encountered in adverse lighting conditions such as direct sunlight and low-light settings in parking lots. The results indicate that YOLOv5 is highly reliable in unobstructed and clear conditions, but its reliability decreases in complex environments. This paper examines potential enhancements and future research directions, such as exploring of alternative model architectures and the implementation of advanced data augmentation techniques, to improve the adaptability and robustness of traffic sign detection systems.
format Article
author Anaz Anizan, Nur Izzati
Ahmat Ruslan, Fazlina
Abd Razak, Noorfadzli
Abdul Aziz, Mohd Azri
Johari, Juliana
author_facet Anaz Anizan, Nur Izzati
Ahmat Ruslan, Fazlina
Abd Razak, Noorfadzli
Abdul Aziz, Mohd Azri
Johari, Juliana
author_sort Anaz Anizan, Nur Izzati
title Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]
title_short Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]
title_full Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]
title_fullStr Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]
title_full_unstemmed Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.]
title_sort performance analysis of the yolov5 model for traffic sign detection / nur izzati anaz anizan ... [et al.]
publisher UiTM Press
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/105787/1/105787.pdf
https://ir.uitm.edu.my/id/eprint/105787/
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score 13.222552