Top

Machine Vision and Applications

Published in:

01-08-2014 | Original Paper

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

Authors: Dorian Schneider, Timm Holtermann, Dorit Merhof

Published in: Machine Vision and Applications | Issue 6/2014

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement

next article Extrinsic calibration of heterogeneous cameras by line images

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

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

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)

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)CrossRef

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

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)CrossRefMATH

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)CrossRef

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

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

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

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)

11.

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

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)CrossRef

14.

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

15.

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

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)CrossRef

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)CrossRefMATH

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)

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)CrossRef

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)CrossRef

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)

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)

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)

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)

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)CrossRef

33.

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

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)CrossRef

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). JanuaryCrossRef

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)CrossRef

37.

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

Title: A traverse inspection system for high precision visual on-loom fabric defect detection
Authors: Dorian Schneider
Timm Holtermann
Dorit Merhof
Publication date: 01-08-2014
Publisher: Springer Berlin Heidelberg
Published in: Machine Vision and Applications / Issue 6/2014
Print ISSN: 0932-8092
Electronic ISSN: 1432-1769
DOI: https://doi.org/10.1007/s00138-014-0600-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 6/2014

Improve scene categorization via sub-scene recognition

Super-resolution: a comprehensive survey

Automatic plant identification from photographs

Two-stage online inference model for traffic pattern analysis and anomaly detection

Fast automatic medical image segmentation based on spatial kernel fuzzy c-means on level set method

Automatic optical phase identification of micro-drill bits based on improved ASM and bag of shape segment in PCB production

Premium Partner