Using Wasserstein distance and conditionality for stable and selective data augmentation in pavement crack analysis
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Shahrestani, Afshin
issued: 2024-10-01T18:59:01Z
issued: 2024
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: Pavement crack detection and treatment is one of the vital tasks in road infrastructure management. Failure to do so in a proactive manner increases maintenance costs and results in a drop in the structural integrity of the pavement. Deep learning techniques promise to automate pavement crack detection, aiding proactive highway maintenance. This approach reduces costs, time, and human bias in defect analysis. One barrier to large-scale adoption of deep learning techniques in this area is the scarcity of high-quality, and balanced training datasets. Inherent data imbalances, where certain crack types predominate over others, lead to detection bias and overfitting in machine learning models. This is exacerbated by variations in crack types, skewing the model training towards more frequently occurring cracks, thereby diminishing efficacy in identifying less common types of cracks. Data augmentation is one of the possible solutions to these issues. Mode Collapse, training instability, and lack of agency in data generation were identified as the main challenges associated with previous pavement crack augmentation methods. To overcome the challenges, the thesis implements two Generative Adversarial Network (GAN) architectures based on the models Wasserstein GAN (WGAN), and Conditional WGAN (C-WGAN) developed in previous literature. These models aim to remedy the shortcomings of previous data augmentation methods used in pavement crack analysis. The research utilizes annotated images from the Crack500 and CrackForest datasets, categorized into transverse, longitudinal, block, and alligator crack types. These images are used as the training data for both the GANs and a baseline classifier, aimed at measuring the impact of synthetic crack images in augmented datasets. The findings demonstrate the effectiveness of WGAN and C-WGAN in addressing the limitations of previous augmentation methods, generating high-quality, diverse synthetic images. The GAN-augmented models achieved…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/89312
note: Using Wasserstein Distance andConditionality for Stable and SelectiveData Augmentation in PavementCrack AnalysisbyAfshin ShahrestaniB.Sc., Ferdowsi University of Mashhad, 2022A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe College of Graduate Studies(Civil Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)September 2024© Afshin Shahrestani 2024The following individuals certify that they have read, and recommend tothe College of Graduate Studies for acceptance, a thesis entitled:Using Wasserstein Distance and Conditionality for Stable and Selective DataAugmentation in Pavement Crack Analysissubmitted by Afshin Shahrestani in partial fulfillment of the requirementsfor the degree of Master of Applied Science.Dr. Suliman Gargoum, School of EngineeringSupervisorDr. Anas Chaaban, School of EngineeringSupervisory Committee MemberDr. Zheng Liu, School of EngineeringSupervisory Committee MemberDr. Yong Gao, Department of Computer ScienceUniversity ExamineriiAbstractPavement crack detection and treatment is one of the vital tasks in road in-frastructure management. Failure to do so in a proactive manner increasesmaintenance costs and results in a drop in the structural integrity of thepavement. Deep learning techniques promise to automate pavement crackdetection, aiding proactive highway maintenance. This approach reducescosts, time, and human bias in defect analysis. One barrier to large-scaleadoption of deep learning techniques in this area is the scarcity of high-quality, and balanced training datasets. Inherent data imbalances, wherecertain crack types predominate over others, lead to detection bias andoverfitting in machine learning models. This is exacerbated by variationsin crack types, skewing the model training towards more frequently occur-ring cracks, thereby diminishing efficacy in identifying less common types ofcracks. Data augmentation is one of the possible solutions to these issues.Mode Collap…
has type: Thesis/Dissertation
date issued: 2024-11
is shown at: 10.14288/1.0445467
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nd/4.0/
scholar level: Graduate
contributor: Gargoum, Suliman
type: Text
identifier URI: http://hdl.handle.net/2429/89312