dc.contributor.advisor |
Fernando S |
|
dc.contributor.author |
Madhusanka VKN |
|
dc.date.accessioned |
2022 |
|
dc.date.available |
2022 |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Madhusanka, V. K. N. (2022). Fabric defect detection using one-class classifier [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21474 |
|
dc.identifier.uri |
http://dl.lib.uom.lk/handle/123/21474 |
|
dc.description.abstract |
Textile is wide, very important in critical industry, because it provide lot prodcut to
the human day to day life. As example cloths, wipes, transportation materials, wipes,
hosuning materials etc. Then quality of the products are very important for their
demand. Therefore defect identification during the production is very importat and
then they can maintain better price for their production. Therefore fabric defect
detection and identification is a very impotant part of the textile industry's quality
control process. Currently, there are many manualinspection method to identify
defects and, to enhance the efficiency, it is needed to repace manual
inspectionmethod bby a automatic inspection method.
Machine vision is diversifying and expanding in defect detection using deep learning.
Traditional systems like detecting and classifying defects using image segmentation,
defect detection and image classification have some limitations like requiring a lot of
defective data to the training process and needing pre-identification of defects in the
datasets. However, it is very difficult to get a large amount of actual data with defects
and real-time processes.
The one-class classifier is a classical machine learning problem that has received
considerable attention recently for fabric defect detection. Tin this scenario, only
non-defective class data are available in the training process and avoid the
requirement of defective to train process. However, State of art models in deep
neural networks with one-class classifiers is still unable to record higher accuracy.
This research proposes our approach, for identifying defective fabric using features
of the non-defective fabric with higher accuracy. The implications of this research
can be an initiative to such applications. That approach consists of a VGG-16
pre-trained framework and trainable network with a new Loss function for increase
accuracy of defect detection. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
FABRIC DEFECT DETECTION - Textile Industry |
en_US |
dc.subject |
ONE-CLASS CLASSIFIER |
en_US |
dc.subject |
DEFECT DETECTION |
en_US |
dc.subject |
ARTIFICIAL INTELLIGENCE -Dissertation |
en_US |
dc.subject |
COMPUTATIONAL MATHEMATICS -Dissertation |
en_US |
dc.subject |
INFORMATION TECHNOLOGY -Dissertation |
en_US |
dc.title |
Fabric defect detection using one-class classifier |
en_US |
dc.type |
Thesis-Abstract |
en_US |
dc.identifier.faculty |
IT |
en_US |
dc.identifier.degree |
MSc in Artificial Intelligence |
en_US |
dc.identifier.department |
Department of Computational Mathematics |
en_US |
dc.date.accept |
2022 |
|
dc.identifier.accno |
TH5008 |
en_US |