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Badanie powierzchni tkanin typu denim za pomocą analizy obrazu w czasie rzeczywistym
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Abstrakty
In spite of using modern weaving technology, many types of fabric defects occur during production. Most defects arising in the production process of a fabric are still detected by human inspection. A machine vision system that can be adapted to different types of fabric inspection machines is proposed in this study. Image frames of denim fabric were acquired using a CCD line-scan camera. An algorithm was developed by using the Gabor filter and double thresholding methods. The performance of the algorithm was tested real-time by analysing a denim fabric sample which contained six types of defects: hole, warp lacking, weft lacking, soiled yarn, water soil and yarn flow (knot). The defective regions of the denim fabric sample were detected and labelled successfully.
Mimo stosowania współczesnych technologii tkackich, wiele typów defektów tkaniny powstaje podczas ich produkcji. Większość defektów podczas procesu produkcji tkaniny w dalszym ciągu jest wykrywana poprzez kontrolę pracowników. W pracy zaproponowano automatyczny system inspekcji wizualnej, który można adoptować do różnych typów maszyn, realizujących inspekcje tkaniny. Wykonano ramki ilustrujące tkaninę typu Denim przy zastosowaniu liniowej kamery skanującej typu CCD. Opracowano algorytm stosując filtr Gabor A i metodę podwójnego progu dyskryminującego. Skuteczność algorytmu testowano analizując próbki tkaniny Denim zawierające 6 typów defektów - dziura, brak osnowy, brak wątku, zabrudzona przędza, zmoczona przędza i pęczki. Sukcesywnie określano zaznaczone uszkodzenia.
Czasopismo
Rocznik
Strony
85--90
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
- Department of Textile Engineering, University of Gaziantep, Gaziantep, Turkey
autor
- Department of Textile Engineering, University of Gaziantep, Gaziantep, Turkey
autor
- Department of Mechanical Engineering, Faculty of Engineering, University of Gaziantep, Gaziantep, Turkey
Bibliografia
- 1. Anagnostopoulos C, Vergados D, Kayafas E, Loumos V, Stassinopoulos G. A computer vision approach for textile quality control. J. of Vis. and Comp. Ani. 2001; 12: 31–44.
- 2. Bodnarova A, Bennamoun M, Latham S. Optimal Gabor filters for textile flaw detection. Pattern Recognit.2002; 35: 2973 – 2991.
- 3. Çelik Hİ, Dülger LC, Topalbekiroğlu M. Development of a machine vision system: realtime fabric defect detection and classification with neural networks, J. Text. Inst. 2014; 105, 6: 575-585.
- 4. Çelik Hİ, Dülger LC, Topalbekiroğlu M. Fabric defect detection using linear filtering and morphological operations. Ind. J. Fib. Text. R. 2014; 39, Sept: 254-259.
- 5. Karayiannis YA, Stojanovic R, Mitropoulos P. Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks. In: The 6th IEEE Int. Conf. on Elect. Circuits and Systems. 1999; 2: 765 – 768.
- 6. Goswami MB, Datta KA. Detecting defects in fabric with laser-based morphological image processing. Text. Res. J. 2000; 70(9): 758-762.
- 7. Mak KL, Peng P, Lau HYK. A real-time computer vision system for detecting defects in textile fabrics. In: IEEE Int. Conf. on Industrial Tech., Hong Kong, China, 2005, pp. 469- 474.
- 8. Han R, Zhang L. Fabric defect detection method based on Gabor filter mask. In: Global Congress on Intell. Systems, Xiamen, China, 2009: 184-188.
- 9. Cho CS, Chung BM, Moo-Jin P. Development of real-time vision-based fabric inspection system. IEEE Trans. on Industrial Elect. 2005; 52: 1073-1079.
- 10. Conci A, Proença CB. A comparison between image-processing approaches to textile inspection. J. Text. Inst. 2000; 91, 2: 317-323.
- 11. Hu MC, Tsai IS. The inspection of fabric defects by using wavelet transform. J. Text. Inst., 2000; 91, 3: 420-443.
- 12. Mak KL, Peng P, Yiu KFC. Fabric defect detection using morphological filters. Image Vision Comput. 2009; 27: 1585–1592.
- 13. Furferi R, Governi L. Machine vision tool for real-time detection of defects on textile raw fabrics. J. Text. Inst. 2008; 99, 1: 57-66.
- 14. Çelik HI. Development of an intelligent fabric defect inspection system. University Of Gaziantep Graduate School Of Natural & Applied Sciences. Ph.D Thesis In Mechanical Engineering, 2013.
- 15. Çelik Hİ, Dülger LC, Topalbekiroğlu M. Developing an algorithm for defect detection of denim fabric: gabor filter method. Teks ve Konf. 2013; 23(2): 255-260.
- 16. Jain AK, Farrokhnia F. Unsupervised texture segmentation using Gabor filters. Pattern Recognit. 1991; 24(12): 1167–1186.
- 17. Porat M, Zeevi YY. The generalized Gabor scheme of image representation in biological and machine vision. IEEE Trans. on Pattern Analysis and Mach. Intell. 1988; 10: 452– 468.
- 18. Bovik AC, Clark M, Geisler WS. Multichannel texture analysis using localized spatial filters. IEEE Trans. on Pattern Analysis and Mach. Intell. 1990; 12, 1: 55–73.
- 19. Kumar A, Pang G. Fabric defect segmentation using multichannel blob detectors. Opt. Eng. 2000; 39(12): 3176-3190.
- 20. Grigorescu SE, Petkov N, Kruizinga P. Comparison of texture features based on Gabor filters. IEEE Transactions on Image Processing 2002; 11, 1: 1160 – 1167.
- 21. The MathWorks, Inc. Create predefined 2-D filter, http://www.mathworks.com/help/toolbox/images/ref/fspecial.html#bqkft1d (accessed 10 June 2012).
- 22. Sonka M, Hlavac V, Boyle R. Image processing, analysis and machine vision international student edition. 3rd. ed. Toronto, 2008: 661-665.
- 23. Solomon C, Breckon T. Fundamentals of digital image processing a practical approach with examples in MATLAB. Chichester, 2011: 200-202.
- 24. Mak KL, Peng P. Detecting defects in textile fabrics with optimal Gabor filters. World Aca. of Sci. Eng. and Tech. 2006; 13: 75-80.
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Bibliografia
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