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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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 |
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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.] |
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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. |
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Article |
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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 |
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2024 |
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https://ir.uitm.edu.my/id/eprint/105787/1/105787.pdf https://ir.uitm.edu.my/id/eprint/105787/ |
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