Jurnal
SINTA 2
Embedded Deep Learning System for Classification of Car Make and Model
Jurnal Ilmu Komputer dan Informasi (JIKI)
ISSN 2502-9274, 2088-7051
Published 2023-03-01
Publisher Faculty of Computer Science - Universitas Indonesia
Language eng
Abstract
Automatic car make, and model classification is essential to support activities of intelligent traffic systems in urban areas, such as surveillance, traffic information collection, statistics, etc. In order to classify this data, we need an embedded system approach for real-time car recognition. Many approaches could be made, from image processing to machine learning. Recently, the development of the Convolutional Neural Network has spurred various research in the Area. ResNet, Inception, DenseNet, and NasNet are some of the most commonly used Neural Network based method that is used to classify images. In this research, these Neural Network methods are going to be compared in classifying vehicle make and model in the Stanford dataset. The dataset contains 196 different labels. Several evaluation metrics are used to compare the performance of the methods. From the experiment, the InceptionV3 method achieved the best performance of the AUROC ratio for training the dataset under 50 epochs. Other methods that achieve a high AUROC value tends to have a higher computational time. Real-time simulations have shown that the embedded system is capable of classifying a 100 % success rate for six concurrent users.
Collaboration network of the authors
17 authors ·
43 co-authorship links
· 13 across different organisations
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Metadata source: OAI-PMH (metadata jurnal) https://jiki.cs.ui.ac.id/index.php/jiki/oai,
first harvested 26 Okt 2026,
last seen 26 Okt 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en-US">Embedded Deep Learning System for Classification of Car Make and Model</dc:title>
<dc:creator>Wibisono, Ari</dc:creator>
<dc:creator>Arief Wisesa, Hanif</dc:creator>
<dc:creator>Bagus Wicaksono, Satria</dc:creator>
<dc:creator>Khatya Fahira, Puteri</dc:creator>
<dc:subject xml:lang="en-US">Embedded System Classification</dc:subject>
<dc:subject xml:lang="en-US">Embedded Deep Learning</dc:subject>
<dc:subject xml:lang="en-US">Car Classification</dc:subject>
<dc:description xml:lang="en-US">Automatic car make, and model classification is essential to support activities of intelligent traffic systems in urban areas, such as surveillance, traffic information collection, statistics, etc. In order to classify this data, we need an embedded system approach for real-time car recognition. Many approaches could be made, from image processing to machine learning. Recently, the development of the Convolutional Neural Network has spurred various research in the Area. ResNet, Inception, DenseNet, and NasNet are some of the most commonly used Neural Network based method that is used to classify images. In this research, these Neural Network methods are going to be compared in classifying vehicle make and model in the Stanford dataset. The dataset contains 196 different labels. Several evaluation metrics are used to compare the performance of the methods. From the experiment, the InceptionV3 method achieved the best performance of the AUROC ratio for training the dataset under 50 epochs. Other methods that achieve a high AUROC value tends to have a higher computational time. Real-time simulations have shown that the embedded system is capable of classifying a 100 % success rate for six concurrent users.</dc:description>
<dc:publisher xml:lang="en-US">Faculty of Computer Science - Universitas Indonesia</dc:publisher>
<dc:date>2023-03-01</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1118</dc:identifier>
<dc:identifier>10.21609/jiki.v16i1.1118</dc:identifier>
<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 16 No. 1 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 69-75</dc:source>
<dc:source>2502-9274</dc:source>
<dc:source>2088-7051</dc:source>
<dc:source>10.21609/jiki.v16i1</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/1118/489</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2023 Jurnal Ilmu Komputer dan Informasi</dc:rights>
</oai_dc:dc>
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Cite this article
APA 7
Wibisono, A., Arief Wisesa, H., Bagus Wicaksono, S., & Khatya Fahira, P. (2023). Embedded Deep Learning System for Classification of Car Make and Model. Jurnal Ilmu Komputer dan Informasi (JIKI), 16(1), 69–75. https://doi.org/10.21609/jiki.v16i1.1118
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IEEE
A. Wibisono, H. Arief Wisesa, S. Bagus Wicaksono, and P. Khatya Fahira, “Embedded Deep Learning System for Classification of Car Make and Model,” Jurnal Ilmu Komputer dan Informasi (JIKI), vol. 16, no. 1, pp. 69–75, 2023, doi: 10.21609/jiki.v16i1.1118.
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