Abstract
Today, deep learning methods are being strongly developed and are used for many different tasks. This paper addresses the task of vehicle detection in parking lots. The focus of this study is to evaluate the performance of several versions of the YOLO (You Only Look Once) object detection algorithm on a self-created dataset, using the models in their default configurations with pre-trained weights from the COCO dataset. The dataset contains various lightning and weather conditions such as sunshine, cloudiness, and the presence of snow. Each YOLO version is evaluated using a range of metrics such as precision, recall, F1 score, and FPS. Methods for optimizing the use of models are then proposed and tested. The results demonstrate the trade-offs between detection accuracy and computational efficiency.
Paper Type
Poster
DOI
10.62036/ISD.2025.84
Vehicle Detection in Parking Lots Using Deep Learning Techniques
Today, deep learning methods are being strongly developed and are used for many different tasks. This paper addresses the task of vehicle detection in parking lots. The focus of this study is to evaluate the performance of several versions of the YOLO (You Only Look Once) object detection algorithm on a self-created dataset, using the models in their default configurations with pre-trained weights from the COCO dataset. The dataset contains various lightning and weather conditions such as sunshine, cloudiness, and the presence of snow. Each YOLO version is evaluated using a range of metrics such as precision, recall, F1 score, and FPS. Methods for optimizing the use of models are then proposed and tested. The results demonstrate the trade-offs between detection accuracy and computational efficiency.
Recommended Citation
Miłośnicki, Ł., Miłośnicki, M. & Zaporowski, S. (2025). Vehicle Detection in Parking Lots Using Deep Learning TechniquesIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.84