On-loom fabric defect detection : state-of-the-art and beyond

@inproceedings{Schneider2015OnloomFD,
  title={On-loom fabric defect detection : state-of-the-art and beyond},
  author={Dorian Schneider and Peter Vary and Dorit Merhof},
  year={2015}
}
Weaving is one of mankind’s oldest crafts. The process of interlacing two sets of yarns in an orthogonal way according a predefined pattern is a technology which is as old as human civilization. Over the centuries, the textile industry evolved into a high-tech industry, characterized by highly sophisticated production machines which operate mostly autonomously and are uncoupled from any human interaction. Built into safety relevant products like airbags, safety belts, fire resistant clothing… CONTINUE READING

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Defect detection in plain weave fabrics by yarn tracking and fully convolutional networks

  • 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
  • 2018
VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

References

Publications referenced by this paper.
SHOWING 1-10 OF 202 REFERENCES

High precision on-loom yarn density measurement in woven fabrics

  • 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings
  • 2014
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

On-loom imaging project webpage.

D. Schneider
  • http://onloom.lfb.rwth-aachen.de,
  • 2014
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Picanol omni plus 800 technical brochure.

P NV
  • 2006
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Ein Referenzdatensatz zur Evaluierung von Sichtprüfverfahren für Textiloberflächen

H. Schulz-Mirbach
  • internal report, Technische Universität Hamburg-Harburg, 1996.
  • 1996
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

A fast method of steel surface defect detection using decision trees applied to LBP based features

  • 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)
  • 2012
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Fabric Texture Analysis Using Computer Vision Techniques

  • IEEE Transactions on Instrumentation and Measurement
  • 2011
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Inspection of metallic surfaces using Local Binary Patterns

  • IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society
  • 2011
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Automatic woven fabric structure identification by using principal component analysis and fuzzy clustering

  • 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings
  • 2010
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Fabric defect detection using morphological filters

  • Image Vision Comput.
  • 2009
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

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