Optimizing Slot Detection by Using Advanced Object Detection Techniques for Intelligent Parking Solutions
Received: 5 April 2025 | Revised: 29 May 2025 and 5 July 2025 | Accepted: 23 July 2025 | Online: 9 February 2026
Corresponding author: Bhavana Narsingoju
Abstract
As the global population continues to rise, the demand for private vehicles and parking spaces has increased, making parking a persistent urban challenge. Locating a vacant parking spot is a common struggle for drivers, particularly during peak hours when multiple individuals search simultaneously. This situation results in several negative consequences, including increased pollution, traffic congestion, higher accident risk, fuel and time waste, and heightened driver frustration. To address these issues, smart parking systems can automatically detect available spaces and guide drivers to the nearest vacant spot efficiently. In this work, we investigated object detection techniques for identifying available parking slots, examining both vacant and occupied spaces, and explored how computer vision, deep learning, and artificial intelligence can enhance intelligent parking management. Methods such as yellow line detection, automatic parking spot inference, You Only Look Once (YOLO)-based car detection, and live analysis visualization were employed in the study, while the PKLot dataset was used for training and testing. The proposed approach achieved a testing accuracy of 99.48%, precision of 99.86%, recall of 99.89%, and F1-score of 99.87% while using 10% images from the PKLot dataset. Additionally, the model achieved 99% accuracy, 97.86% precision, 98.04% recall, and a 98% F1-score when validated on the real-time video segment from a local parking area.
Keywords:
intelligent parking system, deep learning, artificial intelligence, computer vision, object detection, You Only Look Once (YOLO, parking managementDownloads
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