Localization of Cracks in Concrete Structures Lacking Reference Objects and Feature Points Using an Unmanned Aerial Vehicle

Information on the location of cracks in concrete structures is an important factor enabling appropriate maintenance or reinforcement measures to be taken. Most studies related to concrete cracks are limited to crack detection and identification, and studies related to crack location information are...

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Bibliographic Details
Published in:Applied sciences 2023-09, Vol.13 (17), p.9918
Main Authors: Baek, Seung-Chan, Oh, Jintak, Woo, Hyun-Jung, Kim, In-Ho, Jang, Sejun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Concrete
concrete crack
Cracks
Deep learning
Drone aircraft
Efficiency
Image processing
image stitching
Inspections
laser pointer
Lasers
Localization
Machine learning
Methods
Objectivity
Technology application
unmanned aerial vehicle
Unmanned aerial vehicles
Vehicles
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Summary:Information on the location of cracks in concrete structures is an important factor enabling appropriate maintenance or reinforcement measures to be taken. Most studies related to concrete cracks are limited to crack detection and identification, and studies related to crack location information are insufficient. The novelty of this study is to develop application technology related to crack localization by proposing a methodology that can estimate the location of concrete cracks even when reference objects or feature points are lacking using an unmanned aerial vehicle and image processing techniques. For the development and verification of the proposed method, aerial photography and image acquisition were performed using mounting a laser pointer model on an unmanned aerial vehicle. To build the analysis data, image distortion correction and feature point extraction were performed using the homography matrix and scale-invariant feature transform algorithm. Spatial information was established using the point cloud technique and image stitching technique, and crack localization was estimated using generating crack expression data via layer merging. The proposed method was validated using comparison with field-measured data. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13179918