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Detection and Classification of Defects in Knitted Fabric Structures

Department of Textile Engineering, Auburn University, AL 36849-5327, U. S. A.

Yasser Gowayed

Department of Textile Engineering, Auburn University, AL 36849-5327, U. S. A., gowayed{at}auburn.edu

Mohamed Abouiiana

School of Textiles and Materials Technology, Philadelphia University, Philadelphia, PA 19144-5497, U. S. A.

Safinaz Youssef

School of Textiles and Materials Technology, Philadelphia University, Philadelphia, PA 19144-5497, U. S. A.

Christopher Pastore

School of Textiles and Materials Technology, Philadelphia University, Philadelphia, PA 19144-5497, U. S. A.

A new method for knitted fabric defect detection and classification using image analysis and neural networks is presented. Images of six different induced defects were obtained and used in the analysis. Statistical procedures and Fourier Transforms were utilized as two different approaches in the feature extraction effort and neural networks were used to detect and classify the defects. The results showed success in detection and classification of most defects especially when the Fourier transforms technique was utilized.

Key Words: knit fabrics • fabric defects • neural networks • image processing • fast fourier transform

Textile Research Journal, Vol. 76, No. 4, 295-300 (2006)
DOI: 10.1177/0040517506053906


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