209
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Determination of various fabric defects using different machine learning techniques

ORCID Icon, &
Pages 733-743 | Received 03 Feb 2022, Accepted 27 Feb 2023, Published online: 02 May 2023

References

  • Ahmed, N., Natarajan, T., & Rao, K. R. (1974). Discrete cosine transform. IEEE Transactions on Computers, C-23(1), 90–93. https://doi.org/10.1109/T-C.1974.223784
  • Alper Selver, M., Avşar, V., & Özdemir, H. (2014). Textural fabric defect detection using statistical texture transformations and gradient search. The Journal of the Textile Institute, 105(9), 998–1007. https://doi.org/10.1080/00405000.2013.876154
  • Anami, B. S., & Elemmi, M. C. (2022). Comparative analysis of SVM and ANN classifiers for defective and non-defective fabric images classification. The Journal of the Textile Institute, 113(6), 1072–1082. https://doi.org/10.1080/00405000.2021.1915559
  • Arivazhagan, S., Ganesan, L., & Bama, S. (2006). Fault segmentation in fabric images using Gabor wavelet transform. Machine Vision and Applications, 16(6), 356–363. https://doi.org/10.1007/s00138-005-0007-x
  • Arora, P., & Hanmandlu, M. (2022). Detection of defects in fabrics using information set features in comparison with deep learning approaches. The Journal of the Textile Institute, 113(2), 266–272. https://doi.org/10.1080/00405000.2020.1870326
  • Barış, B., & Özek, H. Z. (2019). A study on the systematic classification of woven fabric defects. Journal of Textiles and Engineer, 26(114), 156–167.
  • Basturk, A., Ketencioglu, H., Yugnak, Z., & Yuksel, M. E. (2007). Inspection of defects in fabrics using gabor wavelets and principle component analysis. In 9th International Symposium on Signal Processing and Its Applications, 1–4.
  • Bayram, K. S., Kızrak, M. A., & Bolat, B. (2013). Classification of EEG signals by using support vector machines. In IEEE Inista, 1–3.
  • Behera, B. K., & Mani, M. P. (2007). Characterization and classification of fabric defects using discrete cosine transformation and artificial neural network. Indian Journal of Fibre & Textile Research, 32, 421–426.
  • Büyükkabasakal, K. (2010). Detection and classification of fabric defects. İzmir, Ege University.
  • Çelik, H. İ., Dülger, L. C., & Topalbekiroğlu, M. (2014). Fabric defect detection using linear filtering and morphological operations. Indian Journal of Fibre & Textile Research, 39, 254–259.
  • Cortes, C., & Vapnik, V. (1995). Support vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
  • Dorrity, L., Vachtsevanos, G., & Jasper, W. (1995). Real-time fabric defect detection and control in weaving processes. National Textile Center, G94-2, 143–152.
  • Goldberg, J. B. (1950). Fabric defects - case histories of imperfections in woven cotton and rayon fabrics. Mcgraw-Hill Book Company, Inc.
  • Hanbay, K., Talu, M. F., & Özgüven, Ö. F. (2016). Fabric defect detection systems and methods—A systematic literature review. Optik, 127(24), 11960–11973. https://doi.org/10.1016/j.ijleo.2016.09.110
  • Hanbay, K., Talu, M. F., Özgüven, Ö. F., & Öztürk, D. (2015). Fabric defect detection methods for circular knitting machines. In 23nd Signal Processing and Communications Applications Conference (SIU), 735–738.
  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610–621. https://doi.org/10.1109/TSMC.1973.4309314
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–-778.
  • Huang, C. C., & Chen, I. C. (2001). Neural fuzzy classification for fabric defects. Textile Res. J, 71(3), 220–224. https://doi.org/10.1177/004051750107100306
  • ISO. (1990). Woven Fabrics – Description of defects – Vocabulary. ISO 8498: 1990 (E/F).
  • Jing, J. F., Ma, H., & Zhang, H. H. (2019). Automatic fabric defect detection using a deep convolutional neural network. Coloration Technology, 135(3), 213–223. https://doi.org/10.1111/cote.12394
  • Jmali, M., Zitouni, B., & Sakli, F. (2014). Fabrics defects detecting using image processing and neural networks. In Information and Communication Technologies Innovation and Application (pp. 1–6.) https://doi.org/10.1109/ICTIA.2014.7883765
  • Kavzoğlu, T., & Çölkesen, İ. (2010). Classification of satellite images using decision trees: Kocaeli case. Electronic Journal of Map Technologies, 2(1), 36–45.
  • Kısaoğlu, Ö. (2002). Orta büyüklükte bir dokuma işletmesinde istatistiksel kalite kontrol sisteminin kurulması Dokuz Eylül University Graduate Thesis.
  • Kumar, A. (2003). Neural network based detection of local textile defects. Pattern Recognition, 36(7), 1645–1659. https://doi.org/10.1016/S0031-3203(03)00005-0
  • Kuo, C., & Lee, C. J. (2003). A back propagation neural network for recognizing fabric defects. Textile Res. J, 73(2), 147–151. https://doi.org/10.1177/004051750307300209
  • Li, C., Li, J., Li, Y., He, L., Fu, X., & Chen, J. (2021). Fabric defect detection in textile manufacturing: A survey of the state of the art. Security and Communication Networks, 2021, 1–13. https://doi.org/10.1155/2021/9948808
  • Mottalib, M. M., Rokonuzzaman, M., Habib, M. T., & Ahmed, F. (2015). Fabric defect classification with geometric features using Bayesian classifier. International Conference on Advances in Electrical Engineering, 137–140.
  • Ozkaya, U., Öztürk, Ş., Tuna, K., Seyfi, L., & Akdemir, B. (2018). Faults detection with image processing methods in textile sector. In 1st International Symposium on Innovative Approaches in Scientific Studies, 2, 405–409.
  • Patel, A. (1974). Toward zero defects: Plug profit leak. Meena.
  • Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572. https://doi.org/10.1080/14786440109462720
  • Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. https://doi.org/10.1109/TPAMI.2005.159
  • Rasheed, A., Zafar, B., Rasheed, A., Ali, N., Sajid, M., Dar, S. H., Habib, U., Shehryar, T., & Mahmood, M. T. (2020). Fabric defect detection using computer vision techniques: A comprehensive review. Mathematical Problems in Engineering, 2020, 1–24. https://doi.org/10.1155/2020/8189403
  • Rebhi, A., Benmhammed, I., Abid, S., & Fnaiech, F. (2015). Fabric defect detection using local homogeneity analysis and neural network. Journal of Photonics, 2015, 1–9. https://doi.org/10.1155/2015/376163
  • Sakhare, K., Kulkarni, A., Kumbhakarn, M., & Kare, N. (2015). Spectral and spatial domain approach for fabric defect detection and classification [Paper presentation]. 2015 International Conference on Industrial Instrumentation and Control (ICIC), In 640–644. https://doi.org/10.1109/IIC.2015.7150820
  • Şeker, A., Peker, K. A., Yüksek, A. G., & Delibaş, E. (2016). Fabric defect detection using deep learning. In 24th Signal Processing and Communication Application Conference (SIU), 1437–1440.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1–9.
  • Tellawi, M. (2019). Classification of malaria infected cells using Inception V1. PhD thesis. Near East University .
  • Wei, B., Hao, K., Tang, X. S., & Ding, Y. (2019). A new method using the convolutional neural network with compressive sensing for fabric defect classification based on small sample sizes. Textile Research Journal, 89(17), 3539–3555. https://doi.org/10.1177/0040517518813656
  • Wen, Z., Cao, J., Liu, X., & Ying, S. (2014). Fabric defects detection using adaptive wavelets. International Journal of Clothing Science and Technology, 26(3), 202–211. https://doi.org/10.1108/IJCST-03-2013-0031
  • Yang, X., Pang, G., & Yung, N. (2004). Discriminative training approaches to fabric defect classification based on wavelet transform. Pattern Recognition, 37(5), 889–899. https://doi.org/10.1016/j.patcog.2003.10.011
  • Yaşar, F. G., & Utku, S. (2022). A suggestion system according to fabric control time. Gazi University Journal of Science, 35(4), 1333–1342. https://doi.org/10.35378/gujs.834557
  • Yıldız, K., Buldu, A., & Demetgul, M. (2016). A thermal-based defect classification method in textile fabrics with K-Nearest Neighbor algorithm. Journal of Industrial Textiles, 45(5), 780–795. https://doi.org/10.1177/1528083714555777
  • Zuo, H., Wang, Y., Yang, X., & Wang, X. (2012). Fabric defect detection based on texture enhancement. In 2012 5th International Congress on Image and Signal Processing, 876–880.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.