Poster 20-10

[20-10] Development of Drone-Assisted Pavement Profile Mapping: Near-Surface Void Detection Application.


PI: Javier Irizarry

Co-PI(s): Tarek Rakha

Institution(s): Georgia Institute of Technology


Abstract

Near-surface voids can develop into potholes and spalling causing potential driving hazards. A UAS with dual camera sensors can efficiently collect infrared (IR) and visual (RGB) images. This research studied the application of Computer Vision and Deep Learning to analyze UAV-collected multi-spectrum images to detect areas with voids beneath pavement surfaces.


Please comment below with any statements or questions you may have. Also let GTI if you would be interested webinars or presentations on similar topics.

Add new comment

Filtered HTML

  • Web page addresses and e-mail addresses turn into links automatically.
  • Slideshows can be added to this post.
  • Allowed HTML tags: <a> <em> <strong> <cite> <blockquote> <code> <ul> <ol> <li> <dl> <dt> <dd>

Plain text

  • No HTML tags allowed.
  • Slideshows can be added to this post.
  • Web page addresses and e-mail addresses turn into links automatically.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
2 + 9 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer