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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

34
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
34
Rapidly Varying Flow01:24

Rapidly Varying Flow

27
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
27

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相关实验视频

Updated: May 8, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.2K

基于深度学习的方法,用于在降雪期间检测流量参数.

Cheng Jian1,2, Tiancheng Xie2,3,4, Xiaojian Hu2,3,4

  • 1Nanjing LES Information Technology Co., Ltd., Nanjing 211189, China.

Journal of imaging
|December 27, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的框架,用于分析雪地条件下的交通流量. 该方法提高了车辆检测和交通参数的准确性,改进了智能交通系统.

关键词:
深度学习网络是一个深度学习网络.清除雪 清除雪的方法交通流量参数估计流量参数估计车辆检测 车辆检测 车辆检测虚拟线圈虚拟线圈

更多相关视频

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall
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Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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相关实验视频

Last Updated: May 8, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.2K
Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall
12:21

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall

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Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

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科学领域:

  • 计算机视觉 计算机视觉
  • 智能运输系统 智能运输系统

背景情况:

  • 降雪显著降低视频质量,挑战自动化交通数据收集.
  • 精确的车辆检测和交通参数提取对于智能运输至关重要,特别是在恶劣的天气中.

研究的目的:

  • 开发一个强大的分析框架,从降雪期间拍摄的视频中提取交通流量参数.
  • 为了应对雪造成的图像退化和识别精度降低的挑战.

主要方法:

  • 一个四个阶段的框架,利用深度学习网络去除雪 (颗粒和条纹).
  • 实施YOLOv5用于车辆识别和虚拟线圈方法用于流量参数估计.

主要成果:

  • 在除雪后,在中度雪条件下,车辆识别准确度提高了8.6%.
  • 显著提高了运营速度.
  • 在温和的雪地条件下,在交通流量参数估计中获得了97.2%的准确性.

结论:

  • 拟议的框架有效地克服了雪引起的图像退化,以进行准确的交通分析.
  • 这一进步对于在积雪环境中运行的可靠的智能运输系统至关重要.