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Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin

The increasing use of motorcycles and the corresponding rise in related accidents, particularly among riders not wearing helmets, necessitates an efficient and cost-effective solution for helmet detection. Despite the existence of laws mandating helmet use, enforcement remains a challenge due to the...

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Main Authors: Muhammad Tajuddin, Muhammad Zakwan Arif, Hassan, Noraini, Aminuddin, Raihah
Format: Article
Language:English
Published: College of Computing, Informatics, and Mathematics 2024
Subjects:
Integer programming
Online Access:https://ir.uitm.edu.my/id/eprint/106078/1/106078.pdf
https://ir.uitm.edu.my/id/eprint/106078/
https://fskmjebat.uitm.edu.my/pcmj/
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spelling my.uitm.ir.1060782025-02-26T17:08:18Z https://ir.uitm.edu.my/id/eprint/106078/ Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin Muhammad Tajuddin, Muhammad Zakwan Arif Hassan, Noraini Aminuddin, Raihah Integer programming The increasing use of motorcycles and the corresponding rise in related accidents, particularly among riders not wearing helmets, necessitates an efficient and cost-effective solution for helmet detection. Despite the existence of laws mandating helmet use, enforcement remains a challenge due to the need for manual human assistance in monitoring compliance. This study aims to address this issue by 1) designing a system capable of detecting motorcyclists not wearing helmets and identifying their license plate numbers, 2) developing an automatic detection system using ResNet50 and EasyOCR, and 3) testing the functionality and accuracy of the developed system. The system was trained using a dataset of 500 images sourced from Kaggle, featuring riders both with and without helmets. The application of ResNet50 and EasyOCR demonstrated significant performance in recognizing helmets and license plates across various scenarios. The results indicate that the helmet detection model using ResNet50 has achieved a significant performance with a 90% accuracy rate in recognizing helmets and license plates. Despite certain limitations, this project opens avenues for future research to refine further and advance detection systems, ultimately benefiting users in monitoring and enhancing their safety on the road. College of Computing, Informatics, and Mathematics 2024-10 Article NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/106078/1/106078.pdf Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin. (2024) Progress in Computer and Mathematics Journal (PCMJ) <https://ir.uitm.edu.my/view/publication/Progress_in_Computer_and_Mathematics_Journal_=28PCMJ=29/>, 1. pp. 611-621. ISSN 3030-6728 (Submitted) https://fskmjebat.uitm.edu.my/pcmj/
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 Integer programming
spellingShingle Integer programming
Muhammad Tajuddin, Muhammad Zakwan Arif
Hassan, Noraini
Aminuddin, Raihah
Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin
description The increasing use of motorcycles and the corresponding rise in related accidents, particularly among riders not wearing helmets, necessitates an efficient and cost-effective solution for helmet detection. Despite the existence of laws mandating helmet use, enforcement remains a challenge due to the need for manual human assistance in monitoring compliance. This study aims to address this issue by 1) designing a system capable of detecting motorcyclists not wearing helmets and identifying their license plate numbers, 2) developing an automatic detection system using ResNet50 and EasyOCR, and 3) testing the functionality and accuracy of the developed system. The system was trained using a dataset of 500 images sourced from Kaggle, featuring riders both with and without helmets. The application of ResNet50 and EasyOCR demonstrated significant performance in recognizing helmets and license plates across various scenarios. The results indicate that the helmet detection model using ResNet50 has achieved a significant performance with a 90% accuracy rate in recognizing helmets and license plates. Despite certain limitations, this project opens avenues for future research to refine further and advance detection systems, ultimately benefiting users in monitoring and enhancing their safety on the road.
format Article
author Muhammad Tajuddin, Muhammad Zakwan Arif
Hassan, Noraini
Aminuddin, Raihah
author_facet Muhammad Tajuddin, Muhammad Zakwan Arif
Hassan, Noraini
Aminuddin, Raihah
author_sort Muhammad Tajuddin, Muhammad Zakwan Arif
title Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin
title_short Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin
title_full Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin
title_fullStr Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin
title_full_unstemmed Automated helmet and plates number detection using deep learning / Muhammad Zakwan Arif Muhammad Tajuddin, Noraini Hassan and Raihah Aminuddin
title_sort automated helmet and plates number detection using deep learning / muhammad zakwan arif muhammad tajuddin, noraini hassan and raihah aminuddin
publisher College of Computing, Informatics, and Mathematics
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/106078/1/106078.pdf
https://ir.uitm.edu.my/id/eprint/106078/
https://fskmjebat.uitm.edu.my/pcmj/
_version_ 1825165089108393984
score 13.239859

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