Semantic Segmentation and 3D Reconstruction of Concrete Cracks

Simple item page
dc.contributor.advisorShahbazi, Mozhdeh
dc.contributor.advisorNielsen, John
dc.contributor.authorShokri, Parnia
dc.contributor.committeememberFast, Victoria
dc.contributor.committeememberYanushkevich, Svetlana
dc.contributor.committeememberMacDonald, M. Ethan
dc.date2021-11
dc.date.accessioned2021-07-12T21:38:14Z
dc.date.available2021-07-12T21:38:14Z
dc.date.issued2021-07-05
dc.description.abstractDamage inspection of concrete structures is necessary to prevent disasters and ensure the safety of premises such as buildings, sidewalks, dams and bridges. Cracks are among the most prominent damages in such structures. In this research, a computer vision and machine learning-based solution for identifying and modeling cracks in concrete structures from high-resolution images captured by a stereo camera is proposed. First, using deep learning-based semantic segmentation networks trained on a custom-dataset, crack pixels are identified. Moreover, techniques for improving the accuracy of such networks are developed and evaluated. Second, modifications are applied to the stereo camera’s calibration model to ensure accurate parameter estimation. Finally, two 3D reconstruction methods are proposed, one of which is based on detecting the dominant structural plane surrounding the crack, while the second method focuses on matching the crack pixels across two images. As a result, a 3D model of cracks is produced, from which the cracks' size and other geometric characteristics can be deduced. The solution proposed in this thesis can be used by professionals to regularly inspect concrete structures and make timely maintenance decisions.en_US
dc.identifier.citationShokri, P. (2021). Semantic Segmentation and 3D Reconstruction of Concrete Cracks (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/39006
dc.identifier.urihttp://hdl.handle.net/1880/113625
dc.language.isoengen_US
dc.publisher.facultySchulich School of Engineeringen_US
dc.publisher.institutionUniversity of Calgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.en_US
dc.subjectDeep Learning, Semantic Segmentation, Concrete Cracks, Stereo Vision, 3D Reconstruction, Generative Adversarial Networks, Damage Inspectionen_US
dc.subject.classificationInformation Scienceen_US
dc.subject.classificationRemote Sensingen_US
dc.subject.classificationArtificial Intelligenceen_US
dc.subject.classificationComputer Scienceen_US
dc.subject.classificationEngineering--Civilen_US
dc.subject.classificationEngineering--Electronics and Electricalen_US
dc.subject.classificationEngineering--Environmentalen_US
dc.subject.classificationEngineering--Industrialen_US
dc.subject.classificationRoboticsen_US
dc.titleSemantic Segmentation and 3D Reconstruction of Concrete Cracksen_US
dc.typemaster thesisen_US
thesis.degree.disciplineEngineering – Electrical & Computeren_US
thesis.degree.grantorUniversity of Calgaryen_US
thesis.degree.nameMaster of Science (MSc)en_US
ucalgary.item.requestcopytrueen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ucalgary_2021_shokri_parnia.pdf
Size:
32.07 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.62 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Open Theses and Dissertations