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Comparison of the Use of YOLOv11 Variations in the Empty Parking Spaces Detection System

Sainteknol : Jurnal Sains dan Teknologi · Vol. 23 No. 1 (2025) · pp. 1–10

DOI
10.15294/sainteknol.v23i1.20014
ISSN
2527-3604, 0216-4566
Published
2025-02-03
Publisher
Universitas Negeri Semarang
Language
eng

Abstract

The development of a smart parking system using the You Only Look Once (YOLO) model has improved the efficiency of parking management by providing real-time vehicle detection and availability of parking spaces. This study compared three variations of YOLOv11-Nano (YOLOv11n), YOLOv11-Small (YOLOv11s), and YOLOv11-Medium (YOLOv11m) to determine the most effective model in detecting empty parking spaces. The experiment was carried out using a dataset consisting of 5725 images of parking areas with various conditions such as angles, lighting, and distance. In addition, the researcher also used a 6-second parking lot timelapse video for the test material of the model that had been trained. The results show that each variation of YOLOv11 has its own advantages in terms of accuracy, speed, and computing efficiency. YOLOv11n offers faster detection with lower resource consumption, while YOLOv11m provides higher accuracy with longer processing times. The findings of this study aim to help select the optimal YOLOv11 variant for smart parking implementation, thereby improving efficiency and accuracy in real-world applications.

Keywords

Collaboration network of the authors

22 authors · 57 co-authorship links · 3 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Ahmad Dahlan
  • University of Technology Malaysia
  • Lampung University
  • IPB University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Apriansyah WibowoAffiliation unknown7
Aisya FathimahAffiliation unknown4
Dian Farah SyarifahAffiliation unknown2
Muhammad Irfan ArdiansyahAffiliation unknown2
Annuruddin Tsanasti YassarAffiliation unknown2
Ghesya Arnellia BrillianAffiliation unknown2
Tole SutiknoUniversitas Ahmad Dahlan1
Nik Rumzi Nik IdrisUniversity of Technology Malaysia1
Ahmad Saudi SamosirLampung University1
Nurul FibriantiAffiliation unknown1
Sunu HerawanAffiliation unknown1
Edgina BelvaAffiliation unknown1
Asa Faradiba HilmiAffiliation unknown1
Edi SantosaIPB University1
Syaiful AminAffiliation unknown1
Nandita Novia AnggrainiAffiliation unknown1
I Gede Bagus JayendraAffiliation unknown1
Dhea Yunita UtariAffiliation unknown1
Muhammad RasyiidAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sainteknol/oai, first harvested 6 Nov 2026, last seen 6 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Comparison of the Use of YOLOv11 Variations in the Empty Parking Spaces Detection System</dc:title>
	<dc:creator xml:lang="en">Waskito, Deswal</dc:creator>
	<dc:creator xml:lang="en">Syarifah, Dian Farah </dc:creator>
	<dc:creator xml:lang="en">Aprilianto, Rizky Ajie </dc:creator>
	<dc:subject xml:lang="en">YOLOv11</dc:subject>
	<dc:subject xml:lang="en">smart parking</dc:subject>
	<dc:subject xml:lang="en">object detection</dc:subject>
	<dc:subject xml:lang="en">deep learning</dc:subject>
	<dc:subject xml:lang="en">computer vision</dc:subject>
	<dc:description xml:lang="en">The development of a smart parking system using the You Only Look Once (YOLO) model has improved the efficiency of parking management by providing real-time vehicle detection and availability of parking spaces. This study compared three variations of YOLOv11-Nano (YOLOv11n), YOLOv11-Small (YOLOv11s), and YOLOv11-Medium (YOLOv11m) to determine the most effective model in detecting empty parking spaces. The experiment was carried out using a dataset consisting of 5725 images of parking areas with various conditions such as angles, lighting, and distance. In addition, the researcher also used a 6-second parking lot timelapse video for the test material of the model that had been trained. The results show that each variation of YOLOv11 has its own advantages in terms of accuracy, speed, and computing efficiency. YOLOv11n offers faster detection with lower resource consumption, while YOLOv11m provides higher accuracy with longer processing times. The findings of this study aim to help select the optimal YOLOv11 variant for smart parking implementation, thereby improving efficiency and accuracy in real-world applications.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2025-02-03</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sainteknol/article/view/20014</dc:identifier>
	<dc:identifier>10.15294/sainteknol.v23i1.20014</dc:identifier>
	<dc:source xml:lang="en">Sainteknol : Jurnal Sains dan Teknologi; Vol. 23 No. 1 (2025): June 2025; 1-10</dc:source>
	<dc:source>2527-3604</dc:source>
	<dc:source>0216-4566</dc:source>
	<dc:source>10.15294/sainteknol.v23i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sainteknol/article/view/20014/2961</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2025 Sainteknol : Jurnal Sains dan Teknologi</dc:rights>
</oai_dc:dc>