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Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system

Congestion at Malaysian toll plazas persists due to manual toll rate settings at multiclass lanes, leading to errors and inefficient traffic flow, causing significant economic losses during peak hours. Thus, this project aims to develop the best model detector for an automated vehicle classification...

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Main Authors: Hassan, Raini, Mohd Ridzal, Aisyah Afiqah, Fadzleey, Nur Zulfah Insyirah
Format: Book Chapter
Language:English
Published: Universiti Kuala Lumpur Publishing 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/117392/2/117392_Automated%20Vehicle%20Classification.pdf
http://irep.iium.edu.my/117392/
https://library.unikl.edu.my/publishing/
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spelling my.iium.irep.1173922025-01-08T06:45:20Z http://irep.iium.edu.my/117392/ Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system Hassan, Raini Mohd Ridzal, Aisyah Afiqah Fadzleey, Nur Zulfah Insyirah QA75 Electronic computers. Computer science Congestion at Malaysian toll plazas persists due to manual toll rate settings at multiclass lanes, leading to errors and inefficient traffic flow, causing significant economic losses during peak hours. Thus, this project aims to develop the best model detector for an automated vehicle classification system using computer vision and machine learning algorithms to enhance toll collection efficiency. The methodology involves understanding the business context, acquiring 1,735 images spanning seven vehicle classes, modeling, user evaluation, and deployment using Streamlit and MySQL. Model training utilizes YOLOv8, YOLO-NAS, and Faster R-CNN, with evaluation metrics such as Mean Average Precision (MAP), precision, and others. Key materials include OpenCV, Ultralytics, TensorFlow 2.0, and others. YOLOv8 exhibits superior performance with the highest MAP of 0.995 after fine-tuning compared to other models, demonstrating effectiveness in real-time object detection. The system employs a single detection process, ensuring only one vehicle is detected at a time, enhancing accuracy. The project contributes to the accomplishment of Sustainable Development Goals (SDG), including SDG 11, SDG 9, and SDG 15, supporting sustainable mobility practices. Future enhancements may involve multi- sensor fusion and axle detectors for improved accuracy. Universiti Kuala Lumpur Publishing 2024-12-27 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/117392/2/117392_Automated%20Vehicle%20Classification.pdf Hassan, Raini and Mohd Ridzal, Aisyah Afiqah and Fadzleey, Nur Zulfah Insyirah (2024) Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system. In: Final Year Project Competition & Exhibition (I-CPEX) 2023. Universiti Kuala Lumpur Publishing, Kuala Lumpur, pp. 92-96. https://library.unikl.edu.my/publishing/
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Hassan, Raini
Mohd Ridzal, Aisyah Afiqah
Fadzleey, Nur Zulfah Insyirah
Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system
description Congestion at Malaysian toll plazas persists due to manual toll rate settings at multiclass lanes, leading to errors and inefficient traffic flow, causing significant economic losses during peak hours. Thus, this project aims to develop the best model detector for an automated vehicle classification system using computer vision and machine learning algorithms to enhance toll collection efficiency. The methodology involves understanding the business context, acquiring 1,735 images spanning seven vehicle classes, modeling, user evaluation, and deployment using Streamlit and MySQL. Model training utilizes YOLOv8, YOLO-NAS, and Faster R-CNN, with evaluation metrics such as Mean Average Precision (MAP), precision, and others. Key materials include OpenCV, Ultralytics, TensorFlow 2.0, and others. YOLOv8 exhibits superior performance with the highest MAP of 0.995 after fine-tuning compared to other models, demonstrating effectiveness in real-time object detection. The system employs a single detection process, ensuring only one vehicle is detected at a time, enhancing accuracy. The project contributes to the accomplishment of Sustainable Development Goals (SDG), including SDG 11, SDG 9, and SDG 15, supporting sustainable mobility practices. Future enhancements may involve multi- sensor fusion and axle detectors for improved accuracy.
format Book Chapter
author Hassan, Raini
Mohd Ridzal, Aisyah Afiqah
Fadzleey, Nur Zulfah Insyirah
author_facet Hassan, Raini
Mohd Ridzal, Aisyah Afiqah
Fadzleey, Nur Zulfah Insyirah
author_sort Hassan, Raini
title Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system
title_short Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system
title_full Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system
title_fullStr Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system
title_full_unstemmed Automated Vehicle Classification (AVC) using machine learning implementation in Malaysia's toll system
title_sort automated vehicle classification (avc) using machine learning implementation in malaysia's toll system
publisher Universiti Kuala Lumpur Publishing
publishDate 2024
url http://irep.iium.edu.my/117392/2/117392_Automated%20Vehicle%20Classification.pdf
http://irep.iium.edu.my/117392/
https://library.unikl.edu.my/publishing/
_version_ 1821105140704215040
score 13.235362

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