Open AccessRETRACTED: An Image Processing based Fault Detection in Fabrics
Author(s) -
V. Jacintha,
K. H. Shakthi Murugan,
Karanam Arun Kumar,
Swaroopa Devi,
G. Saravanan,
D Ganesh
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/994/1/012036
Subject(s) - artificial intelligence , computer science , segmentation , computer vision , classifier (uml) , cluster analysis , support vector machine , pattern recognition (psychology) , image segmentation
There are several defect that are occurring in fabrics they occur in the form of a hole, mark, improper stitch, oil stains, missed threads etc. The common thing about these defect are, they cannot be viewed by the naked eye. Hence we firmly say that they defects cannot be effectively identified by manual inspection. However, a much-automated method of inspection is essential. Hence, we are going for computer vision based defect detection. The primary requirement about computer vision is a full-fledged camera that can capture even minute defects. We use K-means clustering along with FCM segmentation in order to segment the flaws efficiently. The classifier used here is SVM classifier, with sensible classification rates of up to 98.9%.