A Sim-to-Real Deformation Classification Pipeline using Data Augmentation and Domain Adaptation

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dc.contributor.authorSol, Joel
dc.contributor.supervisorNajjaran, Homayoun
dc.date.accessioned2024-05-23T20:10:20Z
dc.date.available2024-05-23T20:10:20Z
dc.date.issued2024
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science MASc
dc.description.abstractGeometrical quality assurance is critical for improving manufacturing time and cost. This is more inhibiting when human operators’ visual or haptic assessment is necessary. Modern machine learning (ML) methods can solve this problem but require large datasets with diverse deformations. However, preparing those deformations using physical objects can be difficult and costly. This thesis uses Blender, an opensource simulation tool, to imitate object deformities and automate the preparation of synthetic datasets. The utility of these datasets is improved using two methods; data augmentation such as background randomization and domain adaptation networks. The background randomization approach provides a way to generalize the image distribution to various environments, whereas the domain-adapted approach provides a better-targeted distribution. This thesis showcases that synthetic data created in Blender can be effective for training deformation classification networks. The discrepancies between real and simulated environments can be mitigated to create models for sim-to-real deformation detection.
dc.description.scholarlevelGraduate
dc.identifier.urihttps://hdl.handle.net/1828/16543
dc.languageEnglisheng
dc.language.isoen
dc.rightsAvailable to the World Wide Web
dc.subjectMachine Learning
dc.subjectDomain Adaptation
dc.subjectGenerative Adversarial Networks
dc.subjectSynthetic Data
dc.subjectData Augmentation
dc.subjectDeep Learning
dc.titleA Sim-to-Real Deformation Classification Pipeline using Data Augmentation and Domain Adaptation
dc.typeThesis

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