Vision-Based Open-Space Parking Management System Using YOLOv10

Year 2026, Volume: 2 Issue: 1, 45 - 63, 30.01.2026

Bermal Aratoğlu , Buğra Çelebi , Ahmet Özmen

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

https://izlik.org/JA42BF79CY

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

Vision based parking monitoring • open space management , vehicle tracking , vehicle counting , computer vision , open-air parking space management , real-time monitoring , parking space occupancy

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