Detection and Classification of Defects in Knitted Fabric Structures
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Textile materials and evaluations · Optical measurement and interference techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Textile Engineering, Auburn University, AL 36849-5327, U. S. A.
Department of Textile Engineering, Auburn University, AL 36849-5327, U. S. A.,
School of Textiles and Materials Technology, Philadelphia University, Philadelphia, PA 19144-5497, U. S. A.
References (22)
Show source-provided references
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