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Proceedings Paper

Vision-based in-line fabric defect detection using yarn-specific shape features
Author(s): Dorian Schneider; Til Aach
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Paper Abstract

We develop a methodology for automatic in-line flaw detection in industrial woven fabrics. Where state of the art detection algorithms apply texture analysis methods to operate on low-resolved (~200 ppi) image data, we describe here a process flow to segment single yarns in high-resolved (~1000 ppi) textile images. Four yarn shape features are extracted, allowing a precise detection and measurement of defects. The degree of precision reached allows a classification of detected defects according to their nature, providing an innovation in the field of automatic fabric flaw detection. The design has been carried out to meet real time requirements and face adverse conditions caused by loom vibrations and dirt. The entire process flow is discussed followed by an evaluation using a database with real-life industrial fabric images. This work pertains to the construction of an on-loom defect detection system to be used in manufacturing practice.

Paper Details

Date Published: 2 February 2012
PDF: 10 pages
Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 83000G (2 February 2012); doi: 10.1117/12.907268
Show Author Affiliations
Dorian Schneider, RWTH Aachen (Germany)
Til Aach, RWTH Aachen (Germany)


Published in SPIE Proceedings Vol. 8300:
Image Processing: Machine Vision Applications V
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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