Görüntü işleme yöntemleri kullanarak kumaş dokularında hata tespiti

Öz

Bu çalışmada poplin kumaşta hata tespiti için wiener filtre tabanlı bilgisayar destekli tespit sistemi sunulmaktadır. Hatalı kumaş görüntüleri dijital kamera yardımıyla alınmıştır. Geliştirilen sistem önişleme, bölütleme ve kumaş hata tespiti olarak 3 asamadan oluşmaktadır. On isleme aşaması, dijital görüntülere RGB seviyeden gri seviyeye çevirme ve görüntü iyileştirme adımlarından oluşmaktadır. Bölütleme aşamasında, gri seviye görüntünün arka planı morfolojik işlemler ile bölütlenmiştir. Ardından, bölütlenen görüntü, hata tespit işlemini gerçekleştirmek için ikili seviye görüntüye çevrilmiştir. Kumaş hata tespiti, hata tespit sistemi sırasında wiener filtre kullanılarak gerçeklenmiştir. Wiener filtre ikili seviyedeki görüntüye hatalı olmayan alanların elimine edilmesi amacıyla uygulanmıştır. Gerçeklenen sistem, tespit işlemi için hatalı poplin kumaşlara uygulanmıştır. Elde edilen sonuçlar, geliştirilen algoritmanın farklı tip kumaş hatalarında iyi sonuçlar vermektedir.

Anahtar Kelimeler

• Doku yüzeyi,Hata tespiti,Wiener filtre,Bilgisayar destekli tespit,Bölütleme

Fault detection of fabrics using image processing methods

Öz

This paper presents a computer aided detection (CAD) system which uses wiener filter based approach for detection of defects in poplin fabric. The defective fabric images are taken with the help of the digital camera.  The developed system consists of three phases, including preprocessing, segmentation and detection of fabric defect. In preprocessing phase, a RGB to gray level conversion and image enhancement operations were applied to digital camera images. In segmentation phase, background of the gray level image segmented using morphologic operations. Then, segmented image was converted to binary image to facilitate fabric defect detection process. Fabric defect detection was performed using wiener filter in the detection phase of the system. Wiener filter is applied to binary level image to eliminate structures which are not defect. The developed detection system applied on defective poplin images for detection. The obtained results on different kinds of fabric defects show that the proposed algorithm gives promising results.

Anahtar Kelimeler

• Texture surface,Defect detection,Wiener filter,Computer aided detection,Segmentation

Kaynakça

1. Cho CS, Chung BM, Park MJ. "Development of real-time vision-based fabric inspection system". IEEE Transactions on Industrial Electronics. 52(4), 1073-1079, 2005.
2. Kumar A. "Computer-vision-based fabric defect detection: a survey". IEEE Transactions on Industrial Electronics. 55(1), 348-363, 2008.
3. Chan CH, Pang GK. "Fabric defect detection by Fourier analysis". IEEE transactions on Industry Applications. 36(5), 1267-1276, 2000.
4. Han Y, Shi P. "An adaptive level-selecting wavelet transform for texture defect detection". Image and Vision Computing, 25(8), 1239-1248, 2007.
5. Tsai DM, Hsiao B. "Automatic surface inspection using wavelet reconstruction". Pattern Recognition. 34(6), 1285-1305, 2001.
6. Yang X, Pang G, Yung N. "Robust fabric defect detection and classification using multiple adaptive wavelets". IEE Proceedings-Vision, Image and Signal Processing. 152(6), 715-723,2005.
7. Zhi YX, Pang GK, Yung NHC. "Fabric defect detection using adaptive wavelet". International Conference on Acoustics, Speech and Signal Processing, Salt Lake City, UT, USA , 7-11 May 2001.
8. Hanbay K, Talu MF, Özgüven ÖF. "Fabric defect detection systems and methods-a systematic literature review". Optik-International Journal for Light and Electron Optics. 127(24), 11960-11973,2016.

9. Jie L, Quan H, Mingde B, Fei A. “Fabric Defect Detection Using Adaptively Tuned Gabor Filters". International Journal of Signal Processing, Image Processing and Pattern Recognition. 9(8), 39-58, 2016.
10. Ngan HY, Pang GK, Yung NH. "Automated fabric defect detection-a review". Image and Vision Computing, 29(7), 442-458, 2011.
11. Latif Amet A, Ertüzün A, Erçil A. "An efficient method for texture defect detection: Sub-band domain co-occurrence matrices". Image and Vision computing, 18(6), 543-553, 2000.
12. Rebhi A, Benmhammed I, Abid S, Fnaiech F. “Fabric defect detection using local homogeneity analysis and neural network". Journal of Photonics, 2015, 1-9, 2015.
13. Guzaitis J, Verikas A. "Image analysis and information fusion based defect detection in particleboards". Elektronika ir Elektrotechnika, 71(7), 67-72, 2015.
14. Li P, Zhao Z, Zhang L, Zhang H, Jing J. “The real-time vision system for fabric defect detection with combined approach". 8th International Conference on Image and Graphics, Tianjin, China, 13-16 August 2015.
15. Tsang CS, Ngan HY, Pang GK. "Fabric inspection based on the Elo rating method". Pattern Recognition. 51, 378-394, 2016.
16. Hu GH. "Automated defect detection in textured surfaces using optimal elliptical gabor filters". Optik-International Journal for Light and Electron Optics, 126(14), 1331-1340, 2015.
17. Mathworks. “Mathworks Documentation rgb2gray” http://www.mathworks.com/help/matlab/ref/rgb2gray.html (15.05.2016).
18. Shapiro L, Stockman GC. Computer Vision. 1st ed. NJ, USA, Prentice Hall, 2001.
19. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 2nd ed. 2009.
20. Haralick, R.M.,Shapiro, L.G. Computer and robot vision. Addison-Wesley Longman Publishing Co., Inc.,1991.
21. Mathworks. “Documentation Image Processing Toolbox” http://www.mathworks.com/help/images/ref/im2bw.html (20.05.2016).
22. Demir Ö, Çamurcu AY. "Computer-aided detection of lung nodules using outer surface features". Bio-Medical Materials and Engineering, 26(1), 1213-1222, 2015.
23. Lim JS. Two-dimensional signal and image processing. Englewood Cliffs, NJ, Prentice Hall, 1990.