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 |