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A Method for Defect Detection of Yarn-Dyed Fabric Based on Frequency Domain Filtering and Similarity Measurement

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Języki publikacji
EN
Abstrakty
EN
The detection of defects in yarn-dyed fabric is one of the most difficult problems among the present fabric defect detection methods. The difficulty lies in how to properly separate patterns, textures, and defects in the yarn-dyed fabric. In this paper, a novel automatic detection algorithm is presented based on frequency domain filtering and similarity measurement. First, the separation of the pattern and yarn texture structure of the fabric is achieved by frequency domain filtering technology. Subsequently, segmentation of the periodic units of the pattern is achieved by using distance matching function to measure the fabric pattern. Finally, based on the similarity measurement technology, the pattern’s periodic unit is classified, and thus, automatic detection of the defects in the yarn-dyed fabric is accomplished.
Rocznik
Strony
257--262
Opis fizyczny
Bibliogr. 14 poz.
Twórcy
autor
  • School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • School of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
  • School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
Bibliografia
  • [1] Kumar, A. (2008). Computer vision-based fabric defect detection: a survey. IEEE Transactions on Industrial Electronics, 55(1), 348-363.
  • [2] Ngan, H. Y. T., Pang, G. K. H., Yung N. H. C. (2011). Automated fabric defect detection-A review. Image And Vision Computing, 29(7), 442-458.
  • [3] Bodnarova, A., Bennamoun, M., Latham, S. (2002). Optimal Gabor filters for textile flaw detection. Pattern Recognition, 35(12), 2973-2991.
  • [4] Chan, C. H., Pang, G. K. H. (2000). Fabric defect detection by Fourier analysis. IEEE Transactions on Industry Applications, 36(5), 1267-1276.
  • [5] Ngan, H. Y. T., Pang, G. K. H., Yung, S., Zhang, W. (2005). Wavelet based methods on patterned fabric defect detection. Pattern Recognition, 38(4), 559-576.
  • [6] Ng, M. K., Ngan, H. Y. T., Yuan, X. M., Zhang, W. (2014). Patterned fabric inspection and visualization by the method of image decomposition. IEEE Transactions on Automation Science and Engineering, 11(3), 943-947.
  • [7] Zhang, Y. H., Yuen, C. W. M., Wong, W. K., Kan, C. W. (2011). An intelligent model for detecting and classifying color-textured fabric defects using genetic algorithms and the Elman neural network. Textile Research Journal, 81(17), 1772-1787.
  • [8] Zhang, B., Tang, C. M. (2017). Fabric defect detection based on relative total variation model and adaptive mathematical morphology. Journal of Textile Research, 38(5), 145-149.
  • [9] Li, W. Y., Xue, W. L., Cheng, L. D. (2012). Intelligent detection of defects of yarn dyed fabrics by energy-based local binary patterns. Textile Research Journal, 82(19), 1960-1972.
  • [10] Zhu, D. D., Pan, R. R., Gao, W. D., Zhang, J. (2015). Yarn dyed fabric defect detection based on autocorrelation function and GLCM. Autex Research Journal, 15(3), 226-232.
  • [11] Pan, R. R., Gao, W. D., Li, Z. J., Zhang, J. (2015). Woven fabric density inspection using Fourier image analysis. China Sciencepaper, 10(20), 2417-2421.
  • [12] Oha, G., Leeb, S. Y., Shina, S. Y. (1999). Fast determination of textural periodicity using distance matching function. Pattern Recognition Letters, 20(2), 191-197.
  • [13] Jing, J. F., Yang, P. P., Li, P. F. (2015). Determination on design cycle of printed fabrics based on distance matching function. Journal of Textile Research, 36(12), 98-103.
  • [14] Asha, V., Bhajantri, N. U., Nagabhushan, P. (2014). Similarity measures for automatic defect detection on patterned textures. International Journal of Information and Communication Technology, 4(2-4), 18-21.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a10749e-bfc3-4428-b182-6c46399342b4
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