Embedded Deep Learning System for Classification of Car Make and Model
Dublin Core
Title
Embedded Deep Learning System for Classification of Car Make and Model
Subject
Embedded System Classification, Embedded Deep Learning, Car Classification
Description
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, we utilize pre-processing and cropping technique to maximize the
quality of dataset. Several deep learning networks 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.
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, we utilize pre-processing and cropping technique to maximize the
quality of dataset. Several deep learning networks 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.
Creator
Ari Wibisono, Hanif Arief Wisesa, Satria Bagus Wicaksono, Puteri Khatya Fahira
Source
http://dx.doi.org/10.21609/jiki.v16i1.1118
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2023-02-28
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Ari Wibisono, Hanif Arief Wisesa, Satria Bagus Wicaksono, Puteri Khatya Fahira, “Embedded Deep Learning System for Classification of Car Make and Model,” Repository Horizon University Indonesia, accessed May 22, 2025, https://repository.horizon.ac.id/items/show/8853.