Skip to main content
SpringerLink
Log in

A traverse inspection system for high precision visual on-loom fabric defect detection

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

A self-contained inspection system for vision-based on-loom fabric defect detection is presented in this paper. Design and loom integration of a traversing camera sled, a camera vibration damper and a complementary back-light illumination are presented and discussed. Image acquisition strategies and traverse control are described to complete the discussion on hardware and mechanics. The main part of the paper focuses on a novel algorithmic framework for woven fabric defect detection in highly resolved (1,000+ ppi) image data. Within this scope, single yarns are tracked and measured in terms of position, size, and appearance in real time. An inspection prototype has been mounted onto an industrial loom. Extensive on-line and off-line evaluations for various fabric materials gave precise and stable detection results with few false alarms. A brief cost analysis for the prototype system is provided and completes the presentation of the system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Riethmueller, C.: Thoughts on fabric inspection on the weaving machine. Melliand Text. Engl. 81, 196–E197 (2000)

    Google Scholar 

  2. Mak, K., Peng, P., Lau, H.: A real-time computer vision system for detecting defects in textile fabrics. In: Industrial Technology, 2005. ICIT 2005. IEEE International Conference on 2005, 469–474 (2005)

  3. Castilho, H., Goncalves, P., Pinto, J., Serafim, A.: Intelligent real-time fabric defect detection. In: Kamel, M., Campilho, A. (eds.) Image Analysis and Recognition. Lecture Notes in Computer Science, pp. 1297–1307. Springer, Berlin / Heidelberg (2007)

    Chapter  Google Scholar 

  4. Sari-Sarraf, H., Goddard, J.S.: Vision system for on-loom fabric inspection. Ind. Appl. IEEE Trans. 35(66), 1252–1259 (1999)

    Article  Google Scholar 

  5. Stojanovic, R., Mitropulos, P., Koulamas, C., Karayiannis, Y., Koubias, S., Papadopoulos, G.: Real-time vision-based system for textile fabric inspection. Real-Time Imaging 7(6), 507–518 (2001)

    Article  MATH  Google Scholar 

  6. Cho, C.-S., Chung, B.-M., Park, M.-J.: Development of real-time vision-based fabric inspection system. Ind. Electron. IEEE Trans. 52(4), 1073–1079 (2005)

    Article  Google Scholar 

  7. Shelton, Vision: The shelton webspector. Comput. Control Eng. J. 15(4), 19 (2004)

    Article  Google Scholar 

  8. Elbit Vision Systems Ltd.: IQ-TEX 4 - Vision Empowered Process Monitoring, Product Brochure, published online (2013)

  9. Kumar, A.: Computer-vision-based fabric defect detection: a survey. Ind. Electron. IEEE Trans. 55(1), 348–363 (2008)

    Article  Google Scholar 

  10. Mahajan, P.M., Kolhe, S.R., Patil, P.M.: A review of automatic fabric defect detection techniques. Adv. Comput. Res. 1(2), 18–29 (2009)

    Google Scholar 

  11. Ngan, H.Y., Pang, G.K., Yung, N.H.: Automated fabric defect detection—a review. Image Vision Comput. 29(7), 442–458 (2011)

    Article  Google Scholar 

  12. Raheja, J.L., Kumar, S., Chaudhary, A.: Fabric defect detection based on GLCM and Gabor filter: a comparison. Optik-Int J Light Electron Optics (2013)

  13. Zhang, Y., Lu, Z., Li, J.: Fabric defect detection and classification using Gabor filters and Gaussian Mixture Model. In: Zha, H., Taniguchi, R.-I., Maybank, S. (eds.) Computer Vision ACCV 2009. Lecture Notes in Computer Science, pp. 635–644. Springer, Berlin Heidelberg (2010)

    Chapter  Google Scholar 

  14. Arivazhagan, S., Ganesan, L., Bama, S.: Fault segmentation in fabric images using Gabor wavelet transform. Mach. Vision Appl. 16(6), 356–363 (2006)

    Article  Google Scholar 

  15. Kumar, A., Pang, G.: Defect detection in textured materials using gabor filters. Ind. Appl. IEEE Trans. 38(2), 425–440 (2002)

    Article  Google Scholar 

  16. Srikaew, A., Attakitmongcol, K., Kumsawat, P., Kidsang, W.: Detection of defect in textile fabrics using optimal Gabor Wavelet Network and two-dimensional PCA. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Wang, S., Kyungnam, K., Benes, B., Moreland, K., Borst, C., DiVerdi, S., Yi-Jen, C., Ming, J. (eds.) Advances in Visual Computing. Lecture Notes in Computer Science, pp. 436–445. Springer, Berlin Heidelberg (2011)

    Chapter  Google Scholar 

  17. Kim, S.C., Kang, T.J.: Texture classification and segmentation using wavelet packet frame and Gaussian Mixture Model. Pattern Recognit. 40(4), 1207–1221 (2007)

    Article  MATH  Google Scholar 

  18. Yang, X., Pang, G., Yung, N.: Robust fabric defect detection and classification using multiple adaptive wavelets. Vision, Image and Signal Processing, IEE Proceedings 152(6), 715–723 (2005)

    Google Scholar 

  19. Kim, S., Bae, H., Cheon, S.-P., Kim, K.-B.: On-line fabric-defects detection based on wavelet analysis. In: Gervasi, O., Gavrilova, M., Kumar, V., Lagan, A., Lee, Mun, Y., Taniar, D., Tan, C. (eds.) Computational Science and Its Applications ICCSA 2005. Lecture Notes in Computer Science, pp. 1075–1084. Springer, Berlin Heidelberg (2005)

    Chapter  Google Scholar 

  20. Serdaroglu, A., Ertuzun, A., Ercil, A.: Defect detection in textile fabric images using wavelet transforms and independent component analysis. Pattern Recognit. Image Anal. 16, 61–64 (2006)

    Article  Google Scholar 

  21. NV, P.: Picanol omni plus 800 technical brochure, Web (2006)

  22. Neumann, F., Holtermann, T., Schneider, D., Kulczycki, A., Gries, T., Aach, T.: In-process fault detection for textile fabric production: onloom imaging. Proc. SPIE 8082, 808 240–808 240–12 (2011)

    Google Scholar 

  23. Schneider, D.: Tracking yarns in high resolution fabric images: a real-time approach for online fabric flaw detection. Proc. SPIE 8656, 865 603–865 603–12 (2013)

    Google Scholar 

  24. Zuiderveld, K.: Graphic Gems IV. In: Heckbert P (ed.). Academic Press Professional, San Diego (1994)

  25. Christensen, H., Phillips, J. (eds.) Empirical Evaluation Methods in Computer Vision, ser. World Scientific Press, Machine Perception and Artificial Intelligence. Singapore (July 2002)

  26. Schneider, D., van Ekeris, T., Jacobsmuehlen, J., Gross, S.: On benchmarking non-blind deconvolution algorithms: a sample driven comparison of image de-blurring methods for automated visual inspection systems. In: Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International, pp. 1646–1651 (2013)

  27. Schneider, D., Aach, T.: Vision-based in-line fabric defect detection using yarn-specific shape features. Proc. SPIE 8300, 83 000G–83 000G–10 (2012)

    Google Scholar 

  28. Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application VISSAPP’09. INSTICC Press, pp. 331–340 (2009)

  29. Stockman, G., Shapiro, L.G.: Comput. Vision, 1st edn. Prentice Hall PTR, Upper Saddle River (2001)

    Google Scholar 

  30. Cha, S.-H.: Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1(4), 300–307 (2007). [Online] Available: http://www.gly.fsu.edu/parker/geostats/Cha.pdf

  31. Chinchor, N.: Muc-4 evaluation metrics. In: Proceedings of the 4th conference on Message understanding, ser. MUC4 ’92. Stroudsburg, PA, Association for, Computational Linguistics, USA, pp. 22–29 (1992)

  32. Mak, K., Peng, P., Yiu, K.: Fabric defect detection using morphological filters. Image Vision Comput. 27(10), 1585–1592 (2009)

    Article  Google Scholar 

  33. Ngan, H., Pang, G.: Regularity analysis for patterned texture inspection. Autom. Sci. Eng. IEEE Trans. 6(1), 131–144 (2009)

    Article  Google Scholar 

  34. Ngan, H.Y., Pang, G.K., Yung, S., Ng, M.K.: Wavelet based methods on patterned fabric defect detection. Pattern Recognit. 38(4), 559–576 (2005)

    Article  Google Scholar 

  35. Tajeripour, F., Kabir, E., Sheikhi, A.: Fabric defect detection using modified local binary patterns. EURASIP J. Adv. Signal Process 2008, 60:1–60:12 (2008). January

    Article  Google Scholar 

  36. Abouelela, A., Abbas, H.M., Eldeeb, H., Wahdan, A.A., Nassar, S.M.: Automated vision system for localizing structural defects in textile fabrics. Pattern Recognit. Lett. 26(10), 1435–1443 (2005)

    Article  Google Scholar 

  37. Project website. http://onloom.lfb.rwth-aachen.de

Download references

Author information

Authors and Affiliations

  1. Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany

    Dorian Schneider & Dorit Merhof

  2. Institut für Textiltechnik, RWTH Aachen University, Aachen, Germany

    Timm Holtermann

Authors
  1. Dorian Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timm Holtermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dorit Merhof
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dorian Schneider.

A pseudo-code listings

A pseudo-code listings

figure d

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schneider, D., Holtermann, T. & Merhof, D. A traverse inspection system for high precision visual on-loom fabric defect detection. Machine Vision and Applications 25, 1585–1599 (2014). https://doi.org/10.1007/s00138-014-0600-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-014-0600-y

Keywords

Search

Navigation