SavedAdd your journal

Journals / Textile and Apparel / 2026 / Volume: 28 Issue: 3

YAPAY GÖRME TABANLI KUMAŞ HATA TESPİT SİSTEMİ

Devrim Demiray Soyaslan
İbrahim Karataş
Pages
—
Publication Date
2018-10-01
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Obtained fabric images were converted into histograms by a computer program developed within the scope of this study and defect types were characterized

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Due to the cost and complexity of existing defect detection systems, a fabric defect detection device based on an artificial vision system has been developed in this study.
📊
Key Findings & Data:Using knitted pile fabric, six types of defects were studied: loop drop, fly defect, grease spot, cross- striped defect, hole defect and pilling defect.
💡
Conclusion & Impact:Obtained fabric images were converted into histograms by a computer program developed within the scope of this study and defect types were characterized
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Artificial vision · defect detection · textile

Abstract

Due to the cost and complexity of existing defect detection systems, a fabric defect detection device based on an artificial vision system has been developed in this study. Using knitted pile fabric, six types of defects were studied: loop drop, fly defect, grease spot, cross- striped defect, hole defect and pilling defect. Obtained fabric images were converted into histograms by a computer program developed within the scope of this study and defect types were characterized

🔖

Cite This Article

Soyaslan, D. D., Karataş, İ. (2018). YAPAY GÖRME TABANLI KUMAŞ HATA TESPİT SİSTEMİ. Textile and Apparel, Volume: 28 Issue: 3. https://doi.org/10.32710/tekstilvekonfeksiyon.466847

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (37)

  1. Rallo, M., Sagrario Millan, M., Escofet, J., 2002, Wavelet-based Techniques for Textile Inspection, Grup d’Optica Aplicada I Processament d’Imatge de la Universitat Politecnica de Catalunya, Terrassa.
  2. G.K. Chan., 2016, Fabric Defect Detection by Fourier Analysis, IEEE Trans. Ind. Appl. 36(5), 1267-1276.
  3. H.Y.T. Ngan., G:K.H. Pang., N.H.C. Yung, 2011, Automated Fabric Defect Detection-a Review, Image Visiom Comput. 29, 442-458.
  4. Alam Eldin, A. T.,1988, Computer Vision for Automated Inspection of fabric Products, Ph. D. Thesis, University of Wuppertal, Germany.
  5. Chin, R.T., and Harlow, C.A:, 1982, Automated Visual Inspection: A survey, IEEE Trans. Pattern Anal. Machine Intell., 4(6), 557-573. 5. 6.Turgut, Y., 2013, Yapay Görmeye Dayalı Otomatik Hata Denetim Sistemi, Marmara Üniversitesi, Mekatronik Anabilim Dalı, FBE, Yüksek Lisans Tezi, İstanbul, Türkiye.
  6. Hormes, I., and Wulfhorst, B., 1995, Erkennung der Störpartikel mit Hilfe der digitalen Bildverabeitung Intern. Text.Bull. 41, Garn-u. Flachenherst 2-12.
  7. Watanabe, A., Konda, F., Kurosaki, S.N., 1995, Analysis of Blend Irregularity in Yarns Using Image Processing, Part:III: Evaluation of Blend Irregularity by Line Sense and its Aplication to Actual Blended Yarns, Textile Res. J., 65819, 392.
  8. Yang, W., Lu, S., Wang, S., Li, D., 2011, Fast Recognition of Foreign Fibers in Cotton Lint Using Machine Vision, Mathematical and Computer Modelling, 54, 877-882.
  9. Clark, A, D., Pauri, S, K., Hashim, A, A., 1986, Detection of Defects on Fabrics, IEE Colloquium on ‘’Image Processing for Automated Inspection’’ No:48, London, UK, April.
  10. Jasper, W, J., Potlapalli, H., 1995, Image Analysis of Mispicks in Woven Fabric, Textile research Journal, 65 (11), 683-692.
  11. Ribolzi, S, Merckle, J., Gresser, J., Exbrayat, P, E., 1993, Real time Defect Detection on Textiles using Opto-Electronic Processing, Textile Research Journal, 6382), 61-71.
  12. Shady, E., 1998, A computer Vision Systems for Automated Inspection of Fabrics’’ Master Thesis, Mansaura University, Egypt.
  13. Tsai, I, S., Hu, M, C., 2000, Automatic Inspection of Fabric Defects using an Artificial Neural Network
  14. Textile Handbook 2000, Hong Kong Productivity Council, The Hong Kong Cotton Spinners Association.
  15. Çelik, H.İ., Dülger, C.L., Topalbekiroğlu, M., 2012, Görüntü İşleme Teknikleri Kullanarak Kumaş Hatalarının Belirlenmesi, Elektronic Journal of Textile Technologies, 6, 22-39.
  16. Malek, A.S., 2012, Online Fabric Inspection by Image Processing Technology, Mechanical Engineering, University of Haute Alsace, France.
  17. Yapi, D.,Mejri, M., Allili, S. M., Baaziz, N., 2015, A Learning-Based Approach for Automatic Defect Detection in Textile Images, (IFAC), 48, 2423-2428.
  18. Kumar, A., 2008, Computer Vision-Based Fabric Defect Detection: A Survey, IEEE Transactions on Industrial Electronics, 55, 348-363.
  19. Hanbay, K., Talu, F.M., 2014, Kumaş Hatalarının Online/Ofline Tespit Sistemleri ve Yöntemleri, SAÜ, Fen Bilimleri Dergisi, 18, 46-69.
  20. Semnani, D., Vadood M., 2010, Improvement Of Intelligent Methods For Evaluating The Apparent Quality Of Knitted Fabrics, Engineering Applications of Artificial Intelligence, 23, 217-221.
  21. Abou-iiana, M., Youssef, S., Pastore, C., and Gowayed, Y., 2003, Assesing Structural Changes in Knits during Processing, Textile Research Journal 73(6), 535-540.
  22. Saeidi, R. G., Latifi, M., Najar, S. S., Saeidi A, G., 2005, Computer Vision-Aided Fabric Inspecon System for On-Circular Knitting Machine, Textile Research Journal, 75 (6), 492-497.
  23. Shady, E., Gowayed, Y., Abouiiana, M., Youssef, S., Pastore, C., 2006, Detection and Classification of Defects in Knitted Fabric Structures, Textile Research Journal, 76 (4), 295-300.
  24. Mahajan, P. M., Kolhe, S. R., Pati, P.M., 2009, Areview of Automatic Fabric Defect Detection Techniques, Adv. Comput. Res. 1, 18-29.
  25. Campbell, J.G., and Murtagh, F., 1988, Automatic Visual Inspection of Woven Textiles using a two stage Defect Detector, Opt, Eng. 37, 2536-2542.
  26. Chan, C.H., and Pang, G., 2000, Fabric Defect Detection by Fourier Analysis, IEEE Trans. Ind.Appl. 36, 1267-1276.
  27. Tsai, D.M., and Heish, C. Y., 1999, Automated Surface Inspection for Directional Texturs, Image and Vision Comp., 18, 49-62.
  28. Lambert, G., and Bock, F., 1997, Wavelet Methods for Texture Defect Detection, Proc. IEEE Int. Conf. Image Processing, 3, 201-204.
  29. Mufti, M., 1995, Defect Detection and Identification using Fuzzy Wavelets, PhD Thesis, Georgia Institute of Technology.
  30. Alimohamadi H., Ahmadyfard A., Shojaee E., 2009, Defect Detection in Textiles Using Morphological Analysis of Optimal Gabor Wavelet Filter Response, in: Computer and Automation Engineering, ICCAE '09. International Conference, 26-30.
  31. Han, R., Zhang, L., 2009, Fabric Defect Detection Method Based on Gabor Filter Mask, Intelligent Systems, GCIS '09. WRI Global Congress, 3, 184-188.
  32. Bissi, L.,Baruffa, G., Placidi, P., Ricci, E., Scorzoni, A., Val,g,, P., 2013, Automated Defect Detection In Uniform And Structured Fabrics Using Gabor Filter Sand Pca, Visual Commun Image, 24, 838-845.
  33. Schmitt, R.,Fürjes, T., Abbas, B., Abel, P., Kimmelmann, W., Kosse, P., Buratti, A., 2015, Real-Time Machine Vision System For an Automated Quality Monitaring in Mass Production of Multiaxial Non-Crimp Fabrics, IFAC-Papers Online 48-3, 2393-2398.
  34. Carfagni M, Furferi R, Governi L, 2005, A Real-Time Machine Vision System For Monitoring The Textile Raisng Process, Computers In Industry, 56, 831842.
  35. Abouelela, A., Abbas, H.M., Eldeeb, H., Wahdan, A.A., Nassar, A.M., 2005, Automated Vision System For Localizing Structural Defects In Textile Fabrics, Pattern Recognition. Letters, 26, 1435-1443.
  36. Semnani, D., Vadood M., 2010, Improvement Of Intelligent Methods For Evaluating The Apparent Quality Of Knitted Fabrics, Engineering Applications of Artificial Intelligence, 23, 217-221.
  37. Mufti, M., 1995, Defect Detection and Identification using Fuzzy Wavelets, PhD Thesis, Georgia Institute of Technology.