资源高效设计和实施实时停车监控系统与边缘设备.
Jungyoon Kim1, Incheol Jeong1, Jungil Jung2
1Department of IT Convergence Engineering, Gachon University, Seongnam 13120, Republic of Korea.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了一个高效的智能停车系统,使用边缘计算和双触发机制实时检测车辆占用. 该系统优化了资源使用,并确保了在受限制环境中的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 智慧城市技术 智慧城市技术
背景情况:
- 停车场管理系统面临着高成本和基础设施需求的挑战.
- 边缘计算提供了降低的延迟和运营成本,但在设备计算资源方面面临限制.
- 实时检测车辆占用率对于有效的停车管理至关重要.
研究的目的:
- 为边缘设备开发一个具有成本效益的实时车辆占用检测系统.
- 在智能停车系统中优化计算效率和资源使用.
- 解决在资源有限的环境中实施停车管理的挑战.
主要方法:
- 使用SSD-MobileNetv2用于边缘设备上的车辆占用检测.
- 实现了双触发机制,结合了周期和停车位口罩触发器.
- 从多个IP摄像头处理视频流,用于实时分析.
主要成果:
- 停车位面具触发器显著减少了不必要的AI模型执行.
- 双触发机制确保了连续更新,即使在不稳定的网络条件下.
- 在四个月的时间里,SSD-MobileNetv2实现了0.32秒的处理时间,F1得分为0.9848 .
结论:
- 拟议的系统适用于资源有限的环境中的实时停车管理.
- 智能停车系统为智慧城市提供了一个经济,可行和实用的解决方案.
- 边缘计算与优化的人工智能模型相结合,提高了停车管理的效率.
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