Kim Jungyoon, Jeong Incheol, Jung Jungil, Cho Jinsoo
Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Republic of Korea.
PCT Co., Ltd., Seongnam 13449, Republic of Korea.
Sensors (Basel). 2025 Mar 29;25(7):2181. doi: 10.3390/s25072181.
Parking management systems play a crucial role in addressing parking shortages and operational challenges; however, high initial costs and infrastructure requirements often hinder their implementation. Edge computing offers a promising solution by reducing latency and network traffic, thus optimizing operational costs. Nonetheless, the limited computational resources of edge devices remain a significant challenge. This study developed a real-time vehicle occupancy detection system utilizing SSD-MobileNetv2 on edge devices to process video streams from multiple IP cameras. The system incorporates a dual-trigger mechanism, combining periodic triggers and parking space mask triggers, to optimize computational efficiency and resource usage while maintaining high accuracy and reliability. Experimental results demonstrated that the parking space mask trigger significantly reduced unnecessary AI model executions compared to periodic triggers, while the dual-trigger mechanism ensured consistent updates even under unstable network conditions. The SSD-MobileNetv2 model achieved a frame processing time of 0.32 s and maintained robust detection performance with an F1-score of 0.9848 during a four-month field validation. These findings validate the suitability of the system for real-time parking management in resource-constrained environments. Thus, the proposed smart parking system offers an economical, viable, and practical solution that can significantly contribute to developing smart cities.
停车管理系统在解决停车短缺和运营挑战方面发挥着关键作用；然而，高昂的初始成本和基础设施要求常常阻碍其实施。边缘计算通过减少延迟和网络流量提供了一个有前景的解决方案，从而优化运营成本。尽管如此，边缘设备有限的计算资源仍然是一个重大挑战。本研究开发了一种实时车辆占用检测系统，该系统在边缘设备上利用SSD-MobileNetv2来处理来自多个IP摄像机的视频流。该系统采用了双触发机制，将周期性触发和停车位掩码触发相结合，以在保持高精度和可靠性的同时优化计算效率和资源使用。实验结果表明，与周期性触发相比，停车位掩码触发显著减少了不必要的人工智能模型执行，而双触发机制即使在不稳定的网络条件下也能确保一致的更新。在为期四个月的现场验证期间，SSD-MobileNetv2模型实现了0.32秒的帧处理时间，并以0.9848的F1分数保持了强大的检测性能。这些发现验证了该系统在资源受限环境中用于实时停车管理的适用性。因此，所提出的智能停车系统提供了一种经济、可行且实用的解决方案，可对智慧城市的发展做出重大贡献。
