<![CDATA[The increasing vehicle density in urban areas has made parking space availability a significant challenge. With technological advancements, efficient smart parking systems based on object detection have become essential. This study evaluates the performance of YOLO versions 3 to 11 in detecting vacant parking spaces in urban environments, focusing on real-time processing, high accuracy with limited datasets, and adaptability to varying conditions. Using 4,215 annotated images and two test videos, YOLOv7 achieved the highest overall accuracy of 99.57% with an average FPS of 30.79, making it the most effective model for smart parking applications. YOLOv8 and YOLOv11 followed closely, with accuracies of 98.51% and 98.72%, respectively, and average FPS rates of 32.31 and 31.99, balancing precision and speed, which are ideal for real-time applications. Meanwhile, YOLOv5 stood out for its exceptional processing speed of 33.92 FPS. These results highlight YOLO's potential to revolutionize smart parking systems by significantly enhancing both detection precision and operational efficiency. ]]>
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