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.
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