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应用交通摄像头和基于深度学习的图像分析来预测PM2.5度.

Yanming Liu1, Yuxi Zhang2, Pei Yu1

  • 1School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.

The Science of the total environment
|December 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究将交通摄像头转换为空气质量传感器,用于测量颗粒物 (PM2.5). 这种新的方法利用深度学习以成本效益的方式扩大PM2.5监测覆盖范围.

关键词:
空气质量 空气质量深度学习是一种深度学习.图像分析 图像分析机器学习是机器学习.在PM{2.5) 中,交通摄像头的交通摄像头.

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科学领域:

  • 环境科学 环境科学
  • 计算机科学 计算机科学
  • 人工智能的人工智能

背景情况:

  • 空气污染对全球健康造成重大负担.
  • 现有的空气质量监测网络覆盖范围有限.
  • 监测的成本效益扩大至关重要.

研究的目的:

  • 开发一种用于将交通摄像机转换为颗粒物 (PM2.5) 传感器的新方法.
  • 扩大PM2.5监测覆盖范围,而不会产生额外的基础设施成本.

主要方法:

  • 从参考站收集的交通摄像头图像和每小时的PM2.5数据.
  • 训练了一个神经网络,从图像中获得PM2.5度.
  • 采用随机森林模型进行每小时PM2.5预测.
  • 利用突出地图和特征对模型解释的重要性.

主要成果:

  • 从交通摄像机图像中获得每小时PM2.5的高预测性能 (RMSE:0.76μg/m3,R2:0.98).
  • 模型在不同的天气条件下表现出强度.
  • 突出地图显示专注于不受阻碍的道路表面;特征的重要性突出显示图像意义.

结论:

  • 开发了一种实用的方法,将交通摄像头重新用作PM2.5传感器.
  • 深度学习,特别是Resnet架构,可以扩大PM2.5监测覆盖范围.
  • 这种监控解决方案不需要额外的基础设施.