Spot-Wise Smart Parking: An Edge-Enabled Architecture with YOLOv11 and Digital Twin Integration

2026-02-02

AI Summary

This work proposes an edge computing–based, stall-level intelligent parking architecture to overcome the limitations of traditional systems in achieving precise occupancy monitoring, which hinders advanced applications and user experience. By integrating a YOLOv11m object detection model with a distance-aware matching algorithm and an adaptive bounding box segmentation strategy, the system significantly improves parking space status recognition accuracy under complex real-world conditions. It further constructs a “digital shadow” capable of evolving into a digital twin and repurposes low-cost TV boxes as edge nodes to enable sustainable edge-cloud协同 communication. Evaluated on resource-constrained devices, the system achieves a balanced accuracy of 98.80% with only 8 seconds per inference, successfully supporting real-time, stall-level parking availability queries.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Object Detection & Categorization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 the capabilities of YOLOv11m, a model that has a size of 40.5 MB. In addition, two new components were introduced: (i) a Digital Shadow that visually represents parking lot entities as a base to evolve to a full Digital Twin, and (ii) an application support server based on a repurposed TV box. The latter not only enables scalable communication among cloud services, the parking totem, and a bot that provides detailed spot occupancy statistics, but also promotes hardware reuse as a step towards greater sustainability.

Problem

• smart parking
• spot-wise monitoring
• edge computing
• digital twin
• urban mobility

Innovation

• spot-wise parking
• YOLOv11
• Digital Twin
• edge computing
• Adaptive Bounding Box Partitioning