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High-performance multimodal approach for defect identification in knitted and woven fabric

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dc.contributor.advisor De Silva C
dc.contributor.advisor Sooriyarachchi S
dc.contributor.author Pallemulla PSH
dc.date.accessioned 2022
dc.date.available 2022
dc.date.issued 2022
dc.identifier.citation Pallemulla, P.S.H. (2022). High-performance multimodal approach for defect identification in knitted and woven fabric [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21407
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21407
dc.description.abstract Fabric inspection is a key quality assurance process in the garment industry as it involves the detection of defects in a fabric roll prior to being sent for production. Many studies have been conducted on defect identification in either knitted or woven fabrics, but only a few have considered both types. In this paper, a method for detecting defects in both knitted and woven fabrics is proposed. The method involves extracting co-occurrence, wavelet and local entropy features from a fabric image and classifying the image as defective or defect-free using a classifier with these features given as input. Five commonly-used classifiers were tested. This method was applied to a dataset with seventeen different types of defects and an overall classification accuracy of 93.31% was achieved by the k-nearest neighbours classifier. en_US
dc.language.iso en en_US
dc.subject FABRIC INSPECTION en_US
dc.subject DEFECT DETECTION en_US
dc.subject CO-OCCURRENCE en_US
dc.subject WAVELET en_US
dc.subject LOCAL ENTROPY en_US
dc.subject COMPUTER SCIENCE -Dissertation en_US
dc.subject INFORMATION TECHNOLOGY -Dissertation en_US
dc.title High-performance multimodal approach for defect identification in knitted and woven fabric en_US
dc.type Thesis-Abstract en_US
dc.identifier.faculty Engineering en_US
dc.identifier.degree Master of Philosophy en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.date.accept 2022
dc.identifier.accno TH5060 en_US


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