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ConciseCarNet: convolutional neural network for parking space classification

IAES International Journal of Artificial Intelligence · Vol. 13 No. 4 (2024) · hlm. 4158–4168

DOI
10.11591/ijai.v13.i4.pp4158-4168
ISSN
2252-8938, 2089-4872
Terbit
2024-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The car is a mode of transportation that brings numerous benefits to the community. As a result, the growth of vehicles is increasing, which has a negative impact. Some of the negative impacts include noise, air pollution, traffic congestion, and the need for parking spaces. Drivers that drive around looking for parking places increase the negative impact as well as boredom and even worry for the driver. Therefore, the driver needs this information on the availability of parking spaces. A convolutional neural network (CNN) using a camera is one of the best methods that can be used to solve this problem. We built a more efficient CNN architecture for classifying parking spaces, which was named ConciseCarNet. ConciseCarNet uses 33 and 11 convolution filters, which cause fewer parameters than previous architectures. ConciseCarNet has two branches, each with a different branch structure. This branch is designed to generate additional feature variations, which will help improve the accuracy. Based on testing, the accuracy of ConciseCarNet2x outperforms the accuracy of mAlexnet, Carnet, EfficientParkingNet, and you look once (YOLO)+MobilNet architectures, which is 99.37%. ConciseCarNet has fewer parameters, file sizes, and floating point operations (FLOPs) compared to other architectures.

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  • Medan Area University
  • Universitas Syiah Kuala
  • Universitas Sumatera Utara
  • Meteorological, Climatological, And Geophysical Agency
  • Andalas University
  • Universiti Brunei Darussalam
  • Universiti Teknikal Malaysia Melaka
  • Afiliasi tidak diketahui
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Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Marischa ElvenyUniversitas Sumatera Utara5
Rahmah JoharUniversitas Syiah Kuala2
Mahyuddin K. M. NasutionUniversitas Sumatera Utara2
Habib SatriaMedan Area University2
Syafii SyafiiAndalas University2
Dionikxon ManurungMedan Area University2
MailizarAfiliasi tidak diketahui1
MutiawatiUniversitas Syiah Kuala1
Muhammad ZarlisUniversitas Sumatera Utara1
Syahril EfendiUniversitas Sumatera Utara1
Rizki MulionoMedan Area University1
Muhammad Akbar SiregarMedan Area University1
Mailizar MailizarUniversitas Syiah Kuala1
Mutiawati MutiawatiUniversitas Syiah Kuala1
Nukhe Andri SilvianaMedan Area University1
Indri DayanaMedan Area University1
Dian NoviandriMedan Area University1
Muhammad Khahfi ZuhandaMedan Area University1
Rudi SalamUniversiti Brunei Darussalam1
Achmad Jusuf ZulfikarMedan Area University1
Mohd Yuhazri YaakobUniversiti Teknikal Malaysia Melaka1
Umar MuksinUniversitas Syiah Kuala1
Syamsidik SyamsidikUniversitas Syiah Kuala1
Abdi JihadMeteorological, Climatological, And Geophysical Agency1
Andi Azhar RusdinMeteorological, Climatological, And Geophysical Agency1
Vrieslend Haris BanyunegoroMeteorological, Climatological, And Geophysical Agency1
Yunita IdrisUniversitas Syiah Kuala1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">ConciseCarNet: convolutional neural network for parking space classification</dc:title>
	<dc:creator>Ramli, Marwan</dc:creator>
	<dc:creator>Rahman, Sayuti</dc:creator>
	<dc:creator>Bayu Syah, Rahmad</dc:creator>
	<dc:subject xml:lang="en-US">Neural Network;CNN; Deep Learning</dc:subject>
	<dc:subject xml:lang="en-US">Concise car network; Convolutional neural networks; Deep learning; Parking space; Smart parking system</dc:subject>
	<dc:description xml:lang="en-US">The car is a mode of transportation that brings numerous benefits to the community. As a result, the growth of vehicles is increasing, which has a negative impact. Some of the negative impacts include noise, air pollution, traffic congestion, and the need for parking spaces. Drivers that drive around looking for parking places increase the negative impact as well as boredom and even worry for the driver. Therefore, the driver needs this information on the availability of parking spaces. A convolutional neural network (CNN) using a camera is one of the best methods that can be used to solve this problem. We built a more efficient CNN architecture for classifying parking spaces, which was named ConciseCarNet. ConciseCarNet uses 33 and 11 convolution filters, which cause fewer parameters than previous architectures. ConciseCarNet has two branches, each with a different branch structure. This branch is designed to generate additional feature variations, which will help improve the accuracy. Based on testing, the accuracy of ConciseCarNet2x outperforms the accuracy of mAlexnet, Carnet, EfficientParkingNet, and you look once (YOLO)+MobilNet architectures, which is 99.37%. ConciseCarNet has fewer parameters, file sizes, and floating point operations (FLOPs) compared to other architectures.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2024-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23932</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i4.pp4158-4168</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 4: December 2024; 4158-4168</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23932/14236</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>