Semantic Segmentation and 3D Reconstruction of Concrete Cracks

Damage 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.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Engineering – Electrical & Computer

Grantor dc:publisher.institution

Schulich School of Engineering

Year dc:date.issued

2021

Author dc:creator

• Shokri, Parnia

Advisors dc:contributor.advisor

• Shahbazi, Mozhdeh
• Nielsen, John

Committee members dc:contributor.committeemember

• Fast, Victoria
• Yanushkevich, Svetlana
• MacDonald, M. Ethan

• Deep Learning, Semantic Segmentation, Concrete Cracks, Stereo Vision, 3D Reconstruction, Generative Adversarial Networks, Damage Inspection

Statement dc:rights

• University 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.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/39006

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/113625