Cover Image

A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study

Congestion in toll plazas has prompted the exploration of various solutions, from infrastructure improvements to advanced technologies. Enhancing toll plaza infrastructure, such as constructing additional tollbooths and widening lanes while implementing electronic toll collection systems, has had so...

Full description

Saved in:
Bibliographic Details
Main Authors: Hassan, Raini, Mohd Ridzal, Aisyah Afiqah, Fadzleey, Nur Zulfah Insyirah
Format: Book Chapter
Language:English
Published: KICT Publishing 2024
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/112233/1/112233_A%20machine%20learning-based%20automated%20vehicle.pdf
http://irep.iium.edu.my/112233/
https://kulliyyah.iium.edu.my/kict/fyp-ebook-adict/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.112233
record_format dspace
spelling my.iium.irep.1122332024-05-18T01:57:35Z http://irep.iium.edu.my/112233/ A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study Hassan, Raini Mohd Ridzal, Aisyah Afiqah Fadzleey, Nur Zulfah Insyirah QA75 Electronic computers. Computer science Congestion in toll plazas has prompted the exploration of various solutions, from infrastructure improvements to advanced technologies. Enhancing toll plaza infrastructure, such as constructing additional tollbooths and widening lanes while implementing electronic toll collection systems, has had some positive impacts. However, these existing measures have faced limitations in effectively addressing congestion. The use of mixed-mode lanes at the leftmost toll lanes still applied manual vehicle classification, which relies on human operators, but it has yet to sufficiently overcome congestion, given the diverse vehicle types and toll rates. This situation leads to human error and affects traffic flow. Although RFID (Radio frequency identification) technology has been widely adopted at only a few toll lanes, challenges in implementation have led to congestion issues due to insufficient infrastructure and reliability problems. Therefore, the outcome of this project is to develop the best model detector of automated real-time multiclass vehicle classification for all lanes in the toll plaza. This model input is extracted from a pre-trained 800 images, which consist of 6 classes of vehicles and their annotated XML file, respectively, for one stage detector: Faster Region-Convolutional Neural Network (Faster R-CNN), ResNet-50 and two-stage detectors; You Only Look Once (YOLO), YOLOv8 Darknet-53. The classification model performs well in YOLOv8 architecture with the highest mean average precision (MAP-50) of 95.0% and has a good performance measurement on loss function compared to Faster R-CNN architecture. KICT Publishing 2024-04 Book Chapter NonPeerReviewed application/pdf en http://irep.iium.edu.my/112233/1/112233_A%20machine%20learning-based%20automated%20vehicle.pdf Hassan, Raini and Mohd Ridzal, Aisyah Afiqah and Fadzleey, Nur Zulfah Insyirah (2024) A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study. In: Advancement in ICT: Exploring Innovative Solutions (AdICT) Series 1/2024. KICT Publishing, Kuala Lumpur, Malaysia, pp. 16-35. https://kulliyyah.iium.edu.my/kict/fyp-ebook-adict/
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
A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study
description Congestion in toll plazas has prompted the exploration of various solutions, from infrastructure improvements to advanced technologies. Enhancing toll plaza infrastructure, such as constructing additional tollbooths and widening lanes while implementing electronic toll collection systems, has had some positive impacts. However, these existing measures have faced limitations in effectively addressing congestion. The use of mixed-mode lanes at the leftmost toll lanes still applied manual vehicle classification, which relies on human operators, but it has yet to sufficiently overcome congestion, given the diverse vehicle types and toll rates. This situation leads to human error and affects traffic flow. Although RFID (Radio frequency identification) technology has been widely adopted at only a few toll lanes, challenges in implementation have led to congestion issues due to insufficient infrastructure and reliability problems. Therefore, the outcome of this project is to develop the best model detector of automated real-time multiclass vehicle classification for all lanes in the toll plaza. This model input is extracted from a pre-trained 800 images, which consist of 6 classes of vehicles and their annotated XML file, respectively, for one stage detector: Faster Region-Convolutional Neural Network (Faster R-CNN), ResNet-50 and two-stage detectors; You Only Look Once (YOLO), YOLOv8 Darknet-53. The classification model performs well in YOLOv8 architecture with the highest mean average precision (MAP-50) of 95.0% and has a good performance measurement on loss function compared to Faster R-CNN architecture.
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 A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study
title_short A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study
title_full A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study
title_fullStr A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study
title_full_unstemmed A machine learning-based automated vehicle classification implementation on toll system in Malaysia: a preliminary study
title_sort machine learning-based automated vehicle classification implementation on toll system in malaysia: a preliminary study
publisher KICT Publishing
publishDate 2024
url http://irep.iium.edu.my/112233/1/112233_A%20machine%20learning-based%20automated%20vehicle.pdf
http://irep.iium.edu.my/112233/
https://kulliyyah.iium.edu.my/kict/fyp-ebook-adict/
_version_ 1800081921133248512
score 13.211869

Similar Items