Kumaş hata tespiti ve sınıflandırmada dalgacık dönüşümü ve temel bileşen analizi

Öz

Tekstil endüstrisinde kumaş hataları kalite kontrol elemanları tarafından tespit edilmektedir. Bu süreç hem objektif olmamakta hem de zaman ve maliyet sıkıntısı oluşturmaktadır. Bu çalışmada tekstil endüstrisinde sıklıkla kullanılan kaşe ve kot kumaştan elde edilen görüntüler üzerindeki hataların tespit edilmesi ve sınıflandırılması yapılmıştır. Kalite kontrol makinesi prototipi imal edilip termal görüntüleme yardımıyla hatalı kumaş görüntüleri elde edilmiştir. Termal görüntüler kullanılarak kumaş hataları tespit edilmiş ve sınıflandırılmıştır. İki farklı kumaş türü ile yapılan deneylerde ortalama %95 sınıflama başarısı elde edilmiştir. Deneysel sonuçlara göre kumaş kalite kontrol prosesinin, kumaş kurutma ve fiksleme işleminden sonra ilave bir kalite kontrol basamağı olmaksızın yapılabileceği ortaya konmuştur.

Anahtar Kelimeler

• Termal görüntüleme,Kumaş hataları tespiti,Sınıflandırma,Dalgacık dönüşümü

Wavelet transform and principal component analysis in fabric defect detection and classification

Abstract

Fabric defects are determined by quality control staff in textile industry. This process cannot be performed objectively and it constitutes both time and cost difficulties. In this study the cashmere and denim fabric images which are used often in textile industry are tried in both detection and classification process. Quality control machine prototype has been manufactured then defected fabric images were obtained with the help of thermal imaging. The fabric defects were detected and classified by using the thermal images.  Averagely 95% classification accuracy has been achieved on experiments for two different fabric types.  According to the experimental results, the fabric quality control process can be made after the drying and fixing, without any further quality control step.

Keywords

• Thermal imaging,Fabric fault detection,Classification,Wavelet transform

Kaynakça

1. Mak KL, Peng P. "An automated inspection system for textile fabrics based on Gabor filters". Robotics and Computer-Integrated Manufacturing, 24(3), 359-369,2008.
2. Kısaoğlu Ö. "Kumaş kalite kontrol sistemleri". Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 12(2), 233-241, 2011.
3. Mak K, Peng P, Lau H. "A real-time computer vision system for detecting defects in textile fabrics". International Conference on Industrial Technology, Hong Kong, China, 14-17 December 2005.
4. Chin RT. "Automated visual inspection techniques and applications: A bibliography". Pattern Recognition, 15(4), 343-357, 1982.
5. Jianwei L, Zhang. Y, Jiang J. "Fabric defect detection method based on ımage distance difference". 8th International Conference on Electronic Measurement and Instruments, Xi'an, China, 16-18 August, 2007.
6. Chetverikov D, Hanbury A. "Finding defects in texture using regularity and local orientation". Pattern Recognition, 35(10), 2165-2180,2002.
7. Yang XZ, Pang GK, Yung NH. "Discriminative fabric defect detection using adaptive wavelets". Optical Engineering, 41(12), 3116-3126, 2002.
8. 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.

9. Zhang YF, Bresee RR. "Fabric defect detection and classification using image analysis". Textile Research Journal, 65(1), 1-9,1995.
10. Kumar A, Pang GK. "Defect detection in textured materials using Gabor filters". IEEE Transactions on Industry Applications, 38(2), 425-440,2002.
11. Ebraheem S, Yasser G, Mohamed A, Safinaz Y, Christopher P. "Detection and classification of defects in knitted fabric structures". Textile Research Journal, 76(4), 295-300, 2006.
12. Jing J, Zhang H, Wang J, Li P, Jia J. "Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method". Journal of the Textile Institute, 104(1), 18-27, 2013.
13. Guan S, Shi X, Cui H, Song Y. "Fabric defect detection based on wavelet characteristics". Pacific-Asia Workshop on Computational Intelligence and Industrial Application, Wuhan, China, 19-20 December, 2008.
14. Li Y, Di X. "Fabric defect detection using wavelet decomposition". 3rd International Conference on Consumer Electronics, Communications and Networks (CECNet), Xianning, China, 20-22 November, 2013.
15. Yildiz K, Buldu A, Demetgul M, Yildiz Z. "A novel thermal-based fabric defect detection technique". The Journal of The Textile Institute, 106(3), 275-283, 2015.
16. Ananyev M, Bronin D, Osinkin D, Eremin V, Steinberger-Wilckens R, De Haart L, Mertens J. "Characterization of Ni-cermet degradation phenomena I. Long term resistivity monitoring, image processing and X-ray fluorescence analysis". Journal of Power Sources, 286, 414-426,2015.
17. Çelik HI, Topalbekiroglu M, Dülger L. "Real-Time denim fabric inspection using image analysis". Fibres & Textiles in Eastern Europe, 23(3), 85-90, 2015.
18. Tuceryan M, Jain AK. Texture analysis. The Handbook of Pattern Recognition and Computer Vision, 2, 235-276, 1993.
19. Monadjemi A. Towards Efficient Texture Classification And Abnormality Detection. PhD Thesis, University of Bristol, United Kingdom, 2004.
20. Parker JR. Algorithms for Image Processing and Computer Vision, Indiana, USA , John Wiley & Sons,2010.
21. Xie X. "A review of recent advances in surface defect detection using texture analysis techniques". Electronic Letters on Computer Vision and Image Analysis, 7(3), 2008.
22. Haralick RM. "Statistical and structural approaches to texture". Proceedings of the IEEE, 67(5), 786-804, 1979.
23. Yaycılı A. Temel Bileşenler Analizi için Robust Algoritmalar. Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara, Türkiye, 2006.
24. Sonka M, Hlavac V, Boyle R. Image Processing, Analysis and Machine Vision, 4nd ed. Stamford, USA, Cengage Learning, 2014.
25. Gonzalez RC, Woods RE. Digital Image Processing. Saddle river, NJ, USA, Prentice hall, 2002.
26. Serdaroğlu A, Ertüzün A, Erçil A. "Tekstil kumaş imgelerinde dalgacık dönüşümleri ve bağımsız bileşen analizi ile hata denetimi", Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Kayseri, Türkiye, 2005.
27. Haykin SS. Adaptive Filter Theory. 4nd ed. India, Pearson Education, 2008.
28. Aydemir Ö, Kayıkçıoğlu T. "EEG tabanlı beyin bilgisayar arayüzleri". Akademik Bilişim’09-XI. Akademik Bilişim Konferansı Bildirileri, Şanlıurfa, Türkiye, 11-13 Şubat 2009.
29. Campadelli P, Casiraghi E, Artioli D. "A fully automated method for lung nodule detection from postero-anterior chest radiographs". IEEE Transactions on Medical Imaging, 25(12), 1588-1603,2006.
30. Murphy K, Van Ginneken B, Schilham A, De Hoop B, Gietema H, Prokop M. "A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification". Medical Image Analysis, 13(5), 757-770,2009.
31. Schilham AM, Van Ginneken B, Loog M. “Multi-scale nodule detection in chest radiographs”, International Conference on Medical Image Computing and Computer-Assisted Intervention, 602-609, Berlin, 2003.