Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/227570
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dc.titleFABRIC DEFECT DETECTION WITH DATA-DRIVEN MODELS
dc.contributor.authorZHANG CHEN
dc.date.accessioned2022-06-30T18:00:33Z
dc.date.available2022-06-30T18:00:33Z
dc.date.issued2022-03-30
dc.identifier.citationZHANG CHEN (2022-03-30). FABRIC DEFECT DETECTION WITH DATA-DRIVEN MODELS. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/227570
dc.description.abstractFabric defects bother the clothes manufacturers by causing a considerable loss. A garment factory sent us some textile samples and expected us to offer an automated defect detection strategy to maximize their profit. Hereby, deep learning is proposed to solve the real engineering problem. Data-driven models can identify the fabric defect in real-time with reasonable accuracy. In this project, the best Average Precision (AP) is 31.01% in detecting fabric defects. Two object detection models are implemented in the project. The first model is a variant of Faster Region Proposal-Convolutional Neural Network (Faster R-CNN), modified with a Feature Pyramid Network (FPN). The second model is Retinal Convolutional Neural Network (RetinaNet), a one-stage detector that has a simpler architecture. A well-labeled dataset with five major classes of defects is created carefully in Common Objects in Context (COCO) format. After training, RetinaNet acquires a shorter inference time but also good generalization performance.
dc.language.isoen
dc.subjectFabric defects, Defect detection, Data-driven model, Object Detector, Deep learning
dc.typeThesis
dc.contributor.departmentMECHANICAL ENGINEERING
dc.contributor.supervisorEng Hock Tay
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF ENGINEERING (CDE)
dc.identifier.orcid0000-0001-8469-6153
Appears in Collections:Master's Theses (Open)

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