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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) · hlm. 1–10

DOI
10.15294/sainteknol.v23i1.20014
ISSN
2527-3604, 0216-4566
Terbit
2025-02-03
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

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.

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22 penulis · 58 tautan kepenulisan bersama · 6 lintas organisasi

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  • State University of Semarang
  • Universitas Ahmad Dahlan
  • University of Technology Malaysia
  • Lampung University
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Rekan penulisOrganisasiArtikel bersama
Apriansyah WibowoState University of Semarang6
Aisya FathimahState University of Semarang4
Muhammad Irfan ArdiansyahState University of Semarang3
Annuruddin Tsanasti YassarState University of Semarang3
Ghesya Arnellia BrillianState University of Semarang3
Apriansyah WibowoState University of Semarang2
Tole SutiknoUniversitas Ahmad Dahlan1
Nik Rumzi Nik IdrisUniversity of Technology Malaysia1
Ahmad Saudi SamosirLampung University1
Nurul FibriantiState University of Semarang1
Sunu HerawanState University of Semarang1
Edgina BelvaState University of Semarang1
Asa Faradiba HilmiState University of Semarang1
Syaiful AminState University of Semarang1
Nandita Novia AnggrainiState University of Semarang1
Edi SantosaAfiliasi tidak diketahui1
I Gede Bagus JayendraState University of Semarang1
Dhea Yunita UtariState University of Semarang1
Muhammad RasyiidState University of Semarang1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sainteknol/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>