Automated Defect Detection in Uniform and Structured Fabrics using Gabor Filters and PCA

This paper describes a simple and effective algorithm for texture defect detection in uniform and structured fabrics by exploiting the performance of Gabor filters on the TILDA image database. The proposed approach is structured in feature extraction and defect identification. The feature extraction phase relies on a complex symmetric Gabor filter bank and Principal Component Analysis (PCA). Differently from previous works, our analysis is performed on a patch basis, which has shown to be more effective than considering single pixels as features. The defect identification phase is based on the Euclidean norm of feature vectors, and on the comparison with fabric type specific parameters. The results show that our algorithm outperforms previous approaches in most of the considered 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%.