Spot-Wise Smart Parking: An Edge-Enabled Architecture with YOLOv11 and Digital Twin Integration
Gustavo P. C. P. da Luz, Alvaro M. Aspilcueta Narvaez, Tiago Godoi Bannwart, Gabriel Massuyoshi Sato, Luis Fernando Gomez Gonzalez, Juliana Freitag Borin

TL;DR
This paper presents an edge-enabled smart parking system using YOLOv11 and digital twin integration, achieving high accuracy and real-time spot-level monitoring with innovative spatial matching and hardware reuse.
Contribution
It introduces a spot-wise monitoring strategy with spatial tolerance, an Adaptive Bounding Box Partitioning method, and a Digital Shadow for parking visualization, enhancing existing smart parking solutions.
Findings
Achieves 98.80% accuracy in spot detection
Maintains 8-second inference time on resource-constrained devices
Introduces a scalable, hardware-reusable support server
Abstract
Smart parking systems help reduce congestion and minimize users' search time, thereby contributing to smart city adoption and enhancing urban mobility. In previous works, we presented a system developed on a university campus to 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 and support more advanced applications. To overcome this limitation, we extend the system with a spot-wise monitoring strategy based on a distance-aware matching method with spatial tolerance, enhanced through an Adaptive Bounding Box 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…
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Taxonomy
TopicsSmart Parking Systems Research · Transportation and Mobility Innovations · IoT and Edge/Fog Computing
