边缘计算用于有效和高效的流量表征
Asif Khan1, Khurram S Khattak2, Zawar H Khan1
1Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了一个低成本的边缘计算系统,用于智能城市移动,分析九个交通参数,以增强交通流分析. 该解决方案提供准确的车辆检测和速度估计,改善城市交通管理.
科学领域:
- 城市规划和智能交通系统.
- 计算机视觉和边缘计算应用.
- 实时流量数据分析.
背景情况:
- 有效的交通流分析对于开发智能城市移动解决方案至关重要.
- 现有的交通监控工具通常依赖于有限数量的参数.
- 需要全面,低成本和强大的交通监控系统.
研究的目的:
- 提出一个边缘计算解决方案,以使用九个关键参数进行全面的流量分析.
- 开发一种低成本,易于部署和维护的传感器节点,用于交通监控.
- 评估在现实条件下拟议系统的准确性和运行可行性.
主要方法:
- 使用Raspberry Pi 4,Pi摄像头,Intel Movidius神经计算棒2和4G连接,开发了一个边缘计算传感器节点.
- 从OpenVINO工具包中预先训练的模型被用于车辆检测和分类.
- 一个中心点跟踪算法被实施用于车辆速度估计,数据传输到ThingSpeak云平台.
- 实地测试持续了一个星期,每天分析大约1万辆汽车.
主要成果:
- 该系统在车辆计数方面达到79.8%,分类方面达到93.2%,速度估计方面达到82.9%.
- 传感器节点使用10,000 mAh电源库显示了大约8小时的运行能力.
- 测量所需的数据带宽为1.5 MB/h,表明有效的数据传输.
结论:
- 拟议的边缘计算解决方案通过结合多个参数,有效地克服了现有的交通监控系统的局限性.
- 该系统是一个低成本,强大,准确的解决方案,适合智能城市移动,可以在具有挑战性的环境中运行.
- 这种方法提供了一个可扩展和有效的实时流量分析和管理方法.
关键词:
物联网的物联网,就是物联网.拉斯伯派 (Raspberry Pi) 是一款非常有价值的小米电脑.边缘计算是一种边缘计算.交通监控 交通监控 交通监控城市流动性 城市流动性车辆检测 车辆检测 车辆检测更多相关视频
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