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Automated defect detection in uniform and structured fabrics using Gabor filters and PCA

Publication typeJournal paper
Year of publication2013
AuthorsLucia Bissi, Giuseppe Baruffa, Pisana Placidi, Elisa Ricci, Andrea Scorzoni, and Paolo Valigi
TitleAutomated defect detection in uniform and structured fabrics using Gabor filters and PCA
Journal titleJournal of Visual Communication and Image Representation
DateOctober 2013
ISSN number1047-3203
ISBN number
Key wordsAutomated textile inspection, Fabric defect detection, Gabor filters, Principal Component Analysis, TILDA, Manual defect annotation, Detection rate, False alarm rate
AbstractThis paper describes an algorithm for texture defect detection in uniform and structured fabrics, which has been tested on the TILDA image database. The proposed approach is structured in a feature extraction phase, which relies on a complex symmetric Gabor filter bank and Principal Component Analysis (PCA), and on a defect identification phase, which is based on the Euclidean norm of features and on the comparison with fabric type specific parameters. Our analysis is performed on a patch basis, instead of considering single pixels. The performance has been evaluated with uniformly textured fabrics and fabrics with visible texture and grid-like structures, using as reference defect locations identified by human observers. The results show that our algorithm outperforms previous approaches in most cases, achieving a detection rate of 98.8% and a false alarm rate as low as 0.20–0.37%, whereas for heavily structured yarns misdetection rate can be as low as 5%.
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Last update: 2015-10-12, 16:44:51