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Related Concept Videos

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

Design Example: Alignment of a Road Line Using GIS

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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...
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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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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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Street View Image-Based Road Marking Inspection System Using Computer Vision and Deep Learning Techniques.

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
Summary
This summary is machine-generated.

This study introduces an automated road marking inspection system using computer vision and deep learning. The system accurately detects road marking damage from street view images, improving traffic safety and reducing maintenance burdens.

Keywords:
computer visiondamage detectiondeep learningroad markings

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Infrastructure Maintenance

Background:

  • Road markings are crucial for traffic guidance and safety, but degrade over time.
  • Manual inspection and maintenance of road markings are resource-intensive.
  • Degraded road markings pose risks to both human drivers and autonomous vehicles.

Purpose of the Study:

  • To develop an automated system for inspecting road marking condition using street view imagery.
  • To quantify road marking damage accurately and efficiently.
  • To reduce the economic and human resource burden associated with road marking maintenance.

Main Methods:

  • Utilized computer vision and deep learning techniques on street view images.
  • Employed semantic segmentation, inverse perspective mapping, and image thresholding to calculate damage ratios.
  • Developed a road marking damage detector using the YOLOv11x model.

Main Results:

  • Achieved a mean average precision of 73.5% in detecting road marking damage.
  • Successfully automated the road marking inspection process.
  • Introduced the publicly available Road Marking Damage Detection Dataset (RMDDD).

Conclusions:

  • The proposed system effectively automates road marking inspection, enhancing traffic safety.
  • The developed dataset will support future research in road marking damage detection.
  • This approach offers a scalable and efficient solution for infrastructure maintenance.