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Title	Smartpark Visionaire: Ai-Driven Advanced Parking Slot Detection
ID_Doc	51884
Authors	Santosh N.; Kumar M.S.
Year	2025
Published	Proceedings of 8th International Conference on Inventive Computation Technologies, ICICT 2025
DOI	http://dx.doi.org/10.1109/ICICT64420.2025.11005079
Abstract	The parking conflicts are now held most important among the urban problems leading to its own traffic congestion and wastage of fuel. To relieve the situation, SmartPark is hereby proposed, an intelligent car parking system implementing the YOLOv8 state-of-the-art object detection model capable of identifying parking slots in real-time and accurately. It is trained over a very large dataset of annotated parking lot images by applying different preprocessing and augmentation techniques to further improve the detection accuracy. Performance parameters of the model are determined via measuring mean Average Precision (mAP), confusion matrix analysis, and latency comparison, with the very commendable performance of accuracy and efficiency over the previous YOLO versions. The results strongly signify that SmartPark shows very high precision in detecting free and occupied parking slots, thus greatly reducing time in parking searches and contributing to traffic decongestion. The blend of deep learning and real-time detection supports SmartPark's proposition for smart city infrastructure paving way for automated parking governance and pleasant user experience. In summary, this paper underscores the real-world applicability of YOLOv8 to parking systems that subsequently optimize urban mobility and utilization of resources. © 2025 IEEE.
Author Keywords	AI-driven Parking Management; Automated Parking System; Deep Learning; Intelligent Parking Slot Detection; Real-time Occupancy Monitoring; Smart City Infrastructure; Smart Parking; Traffic Flow Optimization; Weights & Biases (WANDB); YOLOv8

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