Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)

Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)

Automated Toll Collection System (ATCS) is one of the technologies to fulfill the Intelligent Transportation System’s (ITS) aim in providing an efficient road and transportation infrastructure at the expressway. This paper is aimed to provide an accurate and efficient ATCS based on a vehicle type cl...

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Bibliographic Details
Main Authors: Suryanti, Awang, Nik Mohamad Aizuddin, Nik Azmi
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Physics Publishing 2018
Subjects:
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/21980/
http://umpir.ump.edu.my/id/eprint/21980/
http://umpir.ump.edu.my/id/eprint/21980/1/29.%20Automated%20toll%20collection%20system%20based%20on%20vehicle%20type%20classification.pdf
http://umpir.ump.edu.my/id/eprint/21980/2/29.1%20Automated%20toll%20collection%20system%20based%20on%20vehicle%20type%20classification.pdf
id ump-21980
recordtype eprints
spelling ump-219802019-01-15T07:19:18Z http://umpir.ump.edu.my/id/eprint/21980/ Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS) Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi QA76 Computer software Automated Toll Collection System (ATCS) is one of the technologies to fulfill the Intelligent Transportation System’s (ITS) aim in providing an efficient road and transportation infrastructure at the expressway. This paper is aimed to provide an accurate and efficient ATCS based on a vehicle type classification method rather than the current implementation of toll collection that rely on sensor-based and human observation. To fulfill the aim, we proposed to implement SF-CNNLS framework to extract vehicle’s features and classify it into class 1 (passenger vehicle), class 2 (lorry) and class 4 (taxi). This ATCS is aimed to enhance the efficiency of the toll collection in Malaysia. The biggest challenge in this research is how to discriminate features of class 4 as a different class of class 1 since both classes have almost identical features. However, with our proposed method, we able to classify the vehicle with the average accuracy of 90.83 %. We tested our method using a frontal view of a vehicle from the self-obtained database (SPINT) taken using mounted-camera at the toll booth and compare the classification performance with a benchmark database named BIT. Institute of Physics Publishing 2018-07 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21980/1/29.%20Automated%20toll%20collection%20system%20based%20on%20vehicle%20type%20classification.pdf pdf en http://umpir.ump.edu.my/id/eprint/21980/2/29.1%20Automated%20toll%20collection%20system%20based%20on%20vehicle%20type%20classification.pdf Suryanti, Awang and Nik Mohamad Aizuddin, Nik Azmi (2018) Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS). In: Journal of Physics: Conference Series: 2nd International Conference on Artificial Intelligence, Automation and Control Technologies (AIACT 2018), 26 - 29 April 2018 , Osaka, Japan. pp. 1-6., 1061 (1). ISSN 1742-6588 http://iopscience.iop.org/article/10.1088/1742-6596/1061/1/012009/pdf
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
English
topic QA76 Computer software
spellingShingle QA76 Computer software
Suryanti, Awang
Nik Mohamad Aizuddin, Nik Azmi
Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)
description Automated Toll Collection System (ATCS) is one of the technologies to fulfill the Intelligent Transportation System’s (ITS) aim in providing an efficient road and transportation infrastructure at the expressway. This paper is aimed to provide an accurate and efficient ATCS based on a vehicle type classification method rather than the current implementation of toll collection that rely on sensor-based and human observation. To fulfill the aim, we proposed to implement SF-CNNLS framework to extract vehicle’s features and classify it into class 1 (passenger vehicle), class 2 (lorry) and class 4 (taxi). This ATCS is aimed to enhance the efficiency of the toll collection in Malaysia. The biggest challenge in this research is how to discriminate features of class 4 as a different class of class 1 since both classes have almost identical features. However, with our proposed method, we able to classify the vehicle with the average accuracy of 90.83 %. We tested our method using a frontal view of a vehicle from the self-obtained database (SPINT) taken using mounted-camera at the toll booth and compare the classification performance with a benchmark database named BIT.
format Conference or Workshop Item
author Suryanti, Awang
Nik Mohamad Aizuddin, Nik Azmi
author_facet Suryanti, Awang
Nik Mohamad Aizuddin, Nik Azmi
author_sort Suryanti, Awang
title Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)
title_short Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)
title_full Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)
title_fullStr Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)
title_full_unstemmed Automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (SF-CNNLS)
title_sort automated toll collection system based on vehicle type classification using sparse-filtered convolutional neural networks with layer-skipping strategy (sf-cnnls)
publisher Institute of Physics Publishing
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/21980/
http://umpir.ump.edu.my/id/eprint/21980/
http://umpir.ump.edu.my/id/eprint/21980/1/29.%20Automated%20toll%20collection%20system%20based%20on%20vehicle%20type%20classification.pdf
http://umpir.ump.edu.my/id/eprint/21980/2/29.1%20Automated%20toll%20collection%20system%20based%20on%20vehicle%20type%20classification.pdf
first_indexed 2023-09-18T22:32:29Z
last_indexed 2023-09-18T22:32:29Z
_version_ 1777416378532233216

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