Spot-Wise Smart Parking: An Edge-Enabled Architecture with YOLOv11 Towards a Digital Twin

Smart parking systems help reduce congestion and minimize users’ search time, thereby contributing to smart cityadoption and enhancing urban mobility. In previous works, we presented a system developed on a university campusto monitor parking availability by estimating the number of free spaces from vehicle counts within a region of interest.Although this approach achieved good accuracy, it restricted the system’s ability to provide spot-level insights andsupport more advanced applications. To overcome this limitation, we extend the system with a spot-wise monitoringstrategy based on a distance-aware matching method with spatial tolerance, enhanced through an Adaptive BoundingBox Partitioning method for challenging spaces. The proposed approach achieves a balanced accuracy of 98.80%while maintaining an inference time of 8 seconds on a resource-constrained edge device, enhancing the capabilitiesof YOLOv11m, a model that has a size of 40.5 MB. In addition, two new components were introduced: (i) a DigitalShadow that visually represents parking lot entities as a base to evolve to a full Digital Twin, and (ii) an applicationsupport server based on a repurposed TV box. The latter not only enables scalable communication among cloudservices, the parking totem, and a bot that provides detailed spot occupancy statistics, but also promotes hardwarereuse as a step towards greater sustainability.