SmartPark Visionaire: AI-Driven Advanced Parking Slot Detection

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 realtime 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.