Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.7K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
2.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Advanced deep learning framework for underwater object detection with multibeam forward-looking sonar.

Structural health monitoring·2025
Same journal

Deep learning-based obstacle-avoiding autonomous UAVs with fiducial marker-based localization for structural health monitoring.

Structural health monitoring·2024
Same journal

Deep learning-based concrete defects classification and detection using semantic segmentation.

Structural health monitoring·2023
Same journal

Efficient attention-based deep encoder and decoder for automatic crack segmentation.

Structural health monitoring·2022

Related Experiment Video

Updated: Apr 25, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

10.5K

Learning monocular depth estimation for defect measurement from civil RGB-D dataset.

Max Midwinter1, Zaid Abbas Al-Sabbag1, Rishabh Bajaj1

  • 1Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Canada.

Structural Health Monitoring
|April 24, 2026
PubMed
Summary

This study introduces a new method for measuring structural defects using single images. By creating a specialized dataset, it enables accurate 3D reconstruction for infrastructure inspection.

Keywords:
RGB-D datasetVisual inspectiondefect measurementmonocular depth estimation

More Related Videos

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

1.8K

Related Experiment Videos

Last Updated: Apr 25, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
11:34

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

10.5K
Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
05:12

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery

Published on: August 12, 2021

1.8K

Area of Science:

  • Civil Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Civil infrastructure is aging, increasing the need for effective structural inspections.
  • Current inspection methods face challenges due to labor intensity and the difficulty of quantifying defects from single images.
  • Deep learning for image-based defect detection and segmentation shows promise but lacks practical 3D measurement capabilities.

Purpose of the Study:

  • To develop a method for recovering 3D scene geometry from single images for infrastructure defect quantification.
  • To address the lack of specialized datasets for training and evaluating spatial computer vision models in civil engineering.
  • To create a LiDAR-based RGB-D dataset for the civil engineering domain.

Main Methods:

  • Utilized deep learning-based monocular depth estimation to recover 3D geometry from single images.
  • Developed and curated a novel, in situ Light Detection and Ranging (LiDAR) RGB-D dataset specifically for civil engineering applications.
  • Evaluated the proposed monocular depth estimation approach for quantifying defects in civil infrastructure using the new dataset.

Main Results:

  • Successfully demonstrated the recovery of 3D scene geometry from single images.
  • Established a valuable, publicly available LiDAR-based RGB-D dataset for civil engineering research.
  • Validated the practical application of monocular depth estimation for defect quantification in real-world infrastructure scenarios.

Conclusions:

  • Monocular depth estimation offers a viable solution for 3D reconstruction and defect quantification in civil infrastructure from single images.
  • The developed dataset is crucial for advancing spatial computer vision techniques in the civil engineering field.
  • This research facilitates more efficient, accurate, and potentially cost-effective infrastructure inspection processes.