Vision-Based Open-Space Parking Management System Using YOLOv10

Bermal Aratoğlu; Buğra Çelebi; Ahmet Özmen

doi:10.26650/d3ai.1745164

Vision-Based Open-Space Parking Management System Using YOLOv10

Abstract

Efficient parking management has become a critical challenge in modern urban environments and large facilities, such as shopping malls, university campuses, and public spaces. Due to physical insufficiency and the inefficient use of parking lots, the ever-increasing demand for parking spaces cannot be adequately addressed. The proposed system provides real-time monitoring of parking spaces by leveraging existing surveillance camera infrastructure and advanced computer vision techniques. Visual data obtained from video streams are processed at a reduced frame rate to ensure real-time performance, and parking space occupancy is detected using a YOLOv10-X–based object detection model retrained on a custom dataset. The dataset consists of images collected from real-world parking lots under diverse environmental conditions, including different lighting scenarios, weather conditions, and camera viewpoints, and is annotated into two classes: occupied and vacant. Experimental evaluations conducted on multiple real-world video scenarios demonstrate robust performance, achieving overall accuracy values ranging from 92.4% to 99.4%, with precision and recall scores consistently above 90%. These results indicate that the proposed system is a scalable, cost-effective, and reliable solution for real-time parking space monitoring in urban environments.

Keywords

References

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Bermal Aratoğlu ^*
0009-0001-8242-5648
Türkiye

Buğra Çelebi
0009-0000-5705-2678
Türkiye

Ahmet Özmen
0000-0003-2267-2206
Türkiye

Publication Date

January 30, 2026

Submission Date

July 17, 2025

Acceptance Date

December 30, 2025

Published in Issue

Year 2026 Volume: 2 Number: 1

DOI

https://doi.org/10.26650/d3ai.1745164

IZ

https://izlik.org/JA42BF79CY

Cite

RIS / Bibtex

APA

Aratoğlu, B., Çelebi, B., & Özmen, A. (2026). Vision-Based Open-Space Parking Management System Using YOLOv10. Journal of Data Analytics and Artificial Intelligence Applications, 2(1), 45-63. https://doi.org/10.26650/d3ai.1745164

AMA

1.Aratoğlu B, Çelebi B, Özmen A. Vision-Based Open-Space Parking Management System Using YOLOv10. Journal of Data Analytics and Artificial Intelligence Applications. 2026;2(1):45-63. doi:10.26650/d3ai.1745164

Chicago

Aratoğlu, Bermal, Buğra Çelebi, and Ahmet Özmen. 2026. “Vision-Based Open-Space Parking Management System Using YOLOv10”. Journal of Data Analytics and Artificial Intelligence Applications 2 (1): 45-63. https://doi.org/10.26650/d3ai.1745164.

EndNote

Aratoğlu B, Çelebi B, Özmen A (January 1, 2026) Vision-Based Open-Space Parking Management System Using YOLOv10. Journal of Data Analytics and Artificial Intelligence Applications 2 1 45–63.

IEEE

[1]B. Aratoğlu, B. Çelebi, and A. Özmen, “Vision-Based Open-Space Parking Management System Using YOLOv10”, Journal of Data Analytics and Artificial Intelligence Applications, vol. 2, no. 1, pp. 45–63, Jan. 2026, doi: 10.26650/d3ai.1745164.

ISNAD

Aratoğlu, Bermal - Çelebi, Buğra - Özmen, Ahmet. “Vision-Based Open-Space Parking Management System Using YOLOv10”. Journal of Data Analytics and Artificial Intelligence Applications 2/1 (January 1, 2026): 45-63. https://doi.org/10.26650/d3ai.1745164.

JAMA

1.Aratoğlu B, Çelebi B, Özmen A. Vision-Based Open-Space Parking Management System Using YOLOv10. Journal of Data Analytics and Artificial Intelligence Applications. 2026;2:45–63.

MLA

Aratoğlu, Bermal, et al. “Vision-Based Open-Space Parking Management System Using YOLOv10”. Journal of Data Analytics and Artificial Intelligence Applications, vol. 2, no. 1, Jan. 2026, pp. 45-63, doi:10.26650/d3ai.1745164.

Vancouver

1.Bermal Aratoğlu, Buğra Çelebi, Ahmet Özmen. Vision-Based Open-Space Parking Management System Using YOLOv10. Journal of Data Analytics and Artificial Intelligence Applications. 2026 Jan. 1;2(1):45-63. doi:10.26650/d3ai.1745164