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相关概念视频

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

39
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
39
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

33
Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
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相关实验视频

Updated: Jun 4, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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街景基于图像的道路标记检查系统使用计算机视觉和深度学习技术.

Junjie Wu1, Wen Liu2, Yoshihisa Maruyama2

  • 1Nippon Koei Co., Ltd., 5-4 Kojimachi, Chiyoda-ku, Tokyo 102-8539, Japan.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用计算机视觉和深度学习的自动化道路标志检查系统. 该系统准确地检测出街景图像的道路标记损坏,改善交通安全并减少维护负担.

关键词:
计算机视觉 计算机视觉检测损坏检测损坏的检测.深度学习是一种深度学习.道路标志 道路标志 道路标志

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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 基础设施维护 基础设施维护

背景情况:

  • 道路标志对于交通指导和安全至关重要,但随着时间的推移而退化.
  • 手动检查和维护道路标志是资源密集的.
  • 退化的道路标志对人类驾驶员和自动驾驶车辆都有风险.

研究的目的:

  • 开发一种自动化系统,使用街景图像检查道路标志状况.
  • 准确高效地量化道路标记损坏.
  • 减少与道路标志维护相关的经济和人力资源负担.

主要方法:

  • 在街景图像上利用计算机视觉和深度学习技术.
  • 使用语义细分,反向视角映射和图像值来计算损坏比率.
  • 使用YOLOv11x模型开发了一种道路标记损坏探测器.

主要成果:

  • 在检测道路标记损坏时,达到73.5%的平均平均精度.
  • 成功自动化了道路标记检查过程.
  • 引入了公开可用的道路标记损坏检测数据集 (RMDDD).

结论:

  • 拟议的系统有效地自动化了道路标记检查,提高了交通安全.
  • 开发的数据集将支持未来的道路标记损坏检测研究.
  • 这种方法为基础设施维护提供了可扩展和高效的解决方案.