Real-Time Parking Space Management System Based on a Low-Power Embedded Platform

This study proposes an edge-centric outdoor parking management system that performs on-site inference on a low-power embedded device and outputs slot-level occupancy decisions in real time. A dataset comprising 13,691 images was constructed using two cameras capturing frames every 3-5 s under diverse weather and illumination conditions, and a YOLOv8-based detector was trained for vehicle recognition. Beyond raw detections, a temporal occupancy decision module is introduced to map detections to predefined slot regions of interest (ROIs) while applying temporal smoothing and occlusion-robust rules, thereby improving stability under rainy and nighttime conditions. When deployed on an AI-BOX edge platform, the proposed system achieves end-to-end latency p50/p95 of 195 ms and 400 ms, respectively, while sustaining 10 FPS at 3.35 W (2.99 FPS/W) during continuous 24-hour operation. Compared with conventional sensor-based architectures, the proposed design significantly reduces upfront deployment costs and recurring maintenance requirements. Furthermore, when integrated with dynamic pricing mechanisms, it enables accurate and automated fee calculation based on real-time occupancy data. Overall, the results demonstrate that the proposed approach provides a flexible, scalable, and cost-efficient foundation for next-generation smart parking infrastructure.