Incremental learning of deep neural network for robust vehicle classification
Existing single-lane free flow (SLFF) tolling systems either heavily rely on contact-based treadle sensor to detect the number of vehicle wheels or manual operator to classify vehicles. While the former is susceptible to high maintenance cost due to wear and tear, the latter is prone to human er...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2022
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| Online Access: | http://journalarticle.ukm.my/20587/ http://journalarticle.ukm.my/20587/1/11.pdf |
| _version_ | 1848815141751095296 |
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| author | Ahmad Mimi Nathiratul Athriyah, Abdul Kadir Muhammad Amir, Hasan F. M. Zaki, Zainal Abidin Zulkifli, Abdul Rahman Hasbullah, |
| author_facet | Ahmad Mimi Nathiratul Athriyah, Abdul Kadir Muhammad Amir, Hasan F. M. Zaki, Zainal Abidin Zulkifli, Abdul Rahman Hasbullah, |
| author_sort | Ahmad Mimi Nathiratul Athriyah, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | Existing single-lane free flow (SLFF) tolling systems either heavily rely on contact-based treadle sensor to detect the number
of vehicle wheels or manual operator to classify vehicles. While the former is susceptible to high maintenance cost due to
wear and tear, the latter is prone to human error. This paper proposes a vision-based solution to SLFF vehicle classification
by adapting a state-of-the-art object detection model as a backbone of the proposed framework and an incremental training
scheme to train our VehicleDetNet in a continual manner to cater the challenging problem of continuous growing dataset
in real-world environment. It involved four experiment set-ups where the first stage involved CUTe datasets. VehicleDetNet
is utilized for the framework of vehicle detection, and it presents an anchorless network which enable the elimination of the
bounding boxes of candidates’ anchors. The classification of vehicles is performed by detecting the vehicle’s location and
inferring the vehicle’s class. We augment the model with a wheel detector and enumerator to add more robustness, showing
improved performance. The proposed method was evaluated on live dataset collected from the Gombak toll plaza at Kuala
Lumpur-Karak Expressway. The results show that within two months of observation, the mean accuracy increases from 87.3
% to 99.07 %, which shows the efficacy of our proposed method. |
| first_indexed | 2025-11-15T00:45:16Z |
| format | Article |
| id | oai:generic.eprints.org:20587 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:45:16Z |
| publishDate | 2022 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:205872022-11-28T12:19:46Z http://journalarticle.ukm.my/20587/ Incremental learning of deep neural network for robust vehicle classification Ahmad Mimi Nathiratul Athriyah, Abdul Kadir Muhammad Amir, Hasan F. M. Zaki, Zainal Abidin Zulkifli, Abdul Rahman Hasbullah, Existing single-lane free flow (SLFF) tolling systems either heavily rely on contact-based treadle sensor to detect the number of vehicle wheels or manual operator to classify vehicles. While the former is susceptible to high maintenance cost due to wear and tear, the latter is prone to human error. This paper proposes a vision-based solution to SLFF vehicle classification by adapting a state-of-the-art object detection model as a backbone of the proposed framework and an incremental training scheme to train our VehicleDetNet in a continual manner to cater the challenging problem of continuous growing dataset in real-world environment. It involved four experiment set-ups where the first stage involved CUTe datasets. VehicleDetNet is utilized for the framework of vehicle detection, and it presents an anchorless network which enable the elimination of the bounding boxes of candidates’ anchors. The classification of vehicles is performed by detecting the vehicle’s location and inferring the vehicle’s class. We augment the model with a wheel detector and enumerator to add more robustness, showing improved performance. The proposed method was evaluated on live dataset collected from the Gombak toll plaza at Kuala Lumpur-Karak Expressway. The results show that within two months of observation, the mean accuracy increases from 87.3 % to 99.07 %, which shows the efficacy of our proposed method. Penerbit Universiti Kebangsaan Malaysia 2022 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/20587/1/11.pdf Ahmad Mimi Nathiratul Athriyah, and Abdul Kadir Muhammad Amir, and Hasan F. M. Zaki, and Zainal Abidin Zulkifli, and Abdul Rahman Hasbullah, (2022) Incremental learning of deep neural network for robust vehicle classification. Jurnal Kejuruteraan, 34 (5). pp. 843-850. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3405-2022/ |
| spellingShingle | Ahmad Mimi Nathiratul Athriyah, Abdul Kadir Muhammad Amir, Hasan F. M. Zaki, Zainal Abidin Zulkifli, Abdul Rahman Hasbullah, Incremental learning of deep neural network for robust vehicle classification |
| title | Incremental learning of deep neural network for robust vehicle classification |
| title_full | Incremental learning of deep neural network for robust vehicle classification |
| title_fullStr | Incremental learning of deep neural network for robust vehicle classification |
| title_full_unstemmed | Incremental learning of deep neural network for robust vehicle classification |
| title_short | Incremental learning of deep neural network for robust vehicle classification |
| title_sort | incremental learning of deep neural network for robust vehicle classification |
| url | http://journalarticle.ukm.my/20587/ http://journalarticle.ukm.my/20587/ http://journalarticle.ukm.my/20587/1/11.pdf |