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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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Physics Publishing
2018
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| Online Access: | 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 |
| _version_ | 1848821479185055744 |
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| author | Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi |
| author_facet | Suryanti, Awang Nik Mohamad Aizuddin, Nik Azmi |
| author_sort | Suryanti, Awang |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:26:00Z |
| format | Conference or Workshop Item |
| id | ump-21980 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:26:00Z |
| publishDate | 2018 |
| publisher | Institute of Physics Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-219802024-01-08T01:44:30Z 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 (Published) http://iopscience.iop.org/article/10.1088/1742-6596/1061/1/012009/pdf |
| 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) |
| title | 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_short | 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) |
| topic | QA76 Computer software |
| 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 |