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Fabric defect detection based on information entropy and frequency domain saliency

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The automatic detection of defects is an important part of the fabric production process. However, existing methods of detecting defects in fabrics with periodic patterns lack adaptability and perform poorly in detection. In this paper, we propose an unsupervised fabric defect detection method based on the human visual attention mechanism. The method introduces two-dimensional entropy which can reflect the spatial distribution characteristics of images based on one-dimensional entropy, according to the relationship between information entropy and image texture. The image is reconstructed into a quaternion matrix by combining two-dimensional entropy and three feature maps that characterize the opponent color space representation of the input image. The hypercomplex Fourier transform is then used to transform the quaternion image matrix into the frequency domain. We propose a new method for local tuning of amplitude spectrum, thereby suppressing the background pattern while retaining the defect region. Finally, the inverse transform is performed to obtain a saliency map. Through experimental comparisons and a series of numerical evaluations, we demonstrate that the proposed method has a better detection effect compared to state-of-the-art methods in fabric defect detection.

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  1. 1.

    Hanbay, K., Talu, M.F., Ozguven, O.F.: Fabric defect detection systems and methods—a systematic literature review. Optik Int. J. Light Electron Opt. 127(24), 11960–11973 (2016)

  2. 2.

    Ngan, H.Y.T., Pang, G.K.H., Yung, S.P.: Automated fabric defect detection – A review. Image Vis. Comput. 29(7), 442–458 (2011)

  3. 3.

    Makaremi, M., Razmjooy, N., Ramezani, M.: A new method for detecting texture defects based on modified local binary pattern. Signal Image Video Process. 12(7), 1395–1401 (2018)

  4. 4.

    Zhang, Y., Lu, Z., Li, J.: Fabric defect classification using radial basis function network. Pattern Recognit. Lett. 31(13), 2033–2042 (2010)

  5. 5.

    Deng, H., Clausi, D.A.: Gaussian MRF rotation-invariant features for image classification. IEEE Trans. Pattern Anal. Mach. Intell. 26(7), 951–955 (2004)

  6. 6.

    Ma, M., Xie, X., Lam, K.-M., Hu, J., Zhong, Y.: Saliency detection based on singular value decomposition. Vis. Commun. Image Represent. 32, 95–106 (2015)

  7. 7.

    Bissi, L., Giuseppe, B., Placidi, P., Ricci, E., Scorzoni, A., Valigi, P.: Automated defect detection in uniform and structured fabrics using Gabor filters and PCA. J. Vis. Commun. Image Represent. 24(7), 838–845 (2013)

  8. 8.

    Behravan, M., Tajeripour, F., Azimifar, Z., Boostani, R.: Texton-based fabric defect detection and recognition. Iran. J. Electr. Comput. Eng. 10(2), 57–69 (2011)

  9. 9.

    Ng, M.K., Ngan, H.Y.T., Yuan, X., Zhang, W.: Patterned Fabric Inspection and Visualization by the Method of Image Decomposition. IEEE Trans. Autom. Sci. Eng. 11(3), 943–947 (2014)

  10. 10.

    Tsang, C.S.C., Ngan, H.Y.T., Pang, G.K.H.: Fabric inspection based on the ELO rating method. Pattern Recognit. 51, 378–394 (2016)

  11. 11.

    Park, Y., Kweon, I.S.: Ambiguous surface defect image classification of AMOLED displays in smartphones. IEEE Trans. Ind. Informat. 12(2), 597–607 (2016)

  12. 12.

    Cao, J., Wang, N., Zhang, J., Wen, Z., Li, B., Liu, X.: Detection of varied defects in diverse fabric images via modified RPCA with noise term and defect prior. Int. J. Clothing Sci. Technol. 28(4), 516–529 (2016)

  13. 13.

    Cao, J., Zhang, J., Wen, Z., Wang, N., Liu, X.: Fabric defect inspection using prior knowledge guided least squares regression. Multimedia Tools Appl. 76(3), 4141–4157 (2017)

  14. 14.

    Seker, A., Peker, K.A., Yuksek, A.G., Delibas, E.: Fabric defect detection using Deep Learning. In: 24th Signal Processing and Communication Application Conference (SIU), 1437–1440 (2016)

  15. 15.

    Li, Y., Zhao, W., Pan, J.: Deformable patterned fabric defect detection with fisher criterion-based Deep Learning. IEEE Trans. Autom. Sci. Eng. 14(2), 1256–1264 (2017)

  16. 16.

    Ren, R., Hung, T., Tan, K.C.: A generic deep-learning-based approach for automated surface inspection. IEEE Trans. Cybern. 48(3), 929–940 (2018)

  17. 17.

    HuangPeng, Q., et al.: Automatic visual defect detection using texture prior and low-rank representation. IEEE Access. 6, 37965–37976 (2018)

  18. 18.

    Yapi, D., Allili, M.S., Baaziz, N.: Automatic fabric defect detection using learning-based local textural distributions in the contourlet domain. IEEE Trans. Autom. Sci. Eng. 15(3), 1014–1026 (2018)

  19. 19.

    Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proceedings of the International Conference on Neural Inform Processing System. pp. 545–552 (2006)

  20. 20.

    Li, C., Gao, G., Liu, Z., Yu, M., Huang, D.: Fabric defect detection based on biological vision modeling. IEEE Access. 6, 27659–27670 (2018)

  21. 21.

    Zhang, K., Yan, Y., Li, P., Jing, J., Wang, Z.: Fabric Defect Detection Using Salience Metric for Color Dissimilarity and Positional Aggregation. IEEE Access. 6, 49170–49181 (2018)

  22. 22.

    Huang, Y., Qiu, C., Yuan, K.: Surface defect saliency of magnetic tile. Vis. Comput. 1–12 (2018)

  23. 23.

    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998)

  24. 24.

    Gui, Y., Ma, L.: Periodic pattern of texture analysis and synthesis based on texels distribution. Vis Comput. 26, 951–964 (2010)

  25. 25.

    Li, J., Cheng, X., Duan, L., Cheng, X., Huang, T., Tian, Y.: Finding the Secret of Image Saliency in the Frequency Domain. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2428–2440 (2015)

  26. 26.

    Hou, X., Zhang, L.: Saliency detection: a spectral residual approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (CVPR). pp. 1–8 (2007)

  27. 27.

    Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion Fourier transform. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1–8 (2008)

  28. 28.

    Yu, Y., Wang, B., Zhang, L.: Pulse discrete cosine transform for saliency-based visual attention. In: Proceedings of the IEEE 8th International Conference on Development and Learning (ICDL), pp. 41–46 (2009)

  29. 29.

    Jung, C., Kim, C.: A unified spectral-domain approach for saliency detection and its application to automatic object segmentation. IEEE Trans. Image Process. 21(3), 1272–1283 (2011)

  30. 30.

    Li, J., Levine, M.D., An, X., Xu, X., He, H.: Visual saliency based on scale-space analysis in the frequency domain. IEEE Trans. Pattern Anal. Mach. Intell. 35(4), 996–1010 (2013)

  31. 31.

    Chen, D., Jia, T., Wu, C.: Visual Saliency Detection: from Space to Frequency. Signal Process Image Commun. 44(5), 57–68 (2016)

  32. 32.

    Ell, T.A., Sangwine, S.J.: Hypercomplex Fourier transforms of color images. IEEE Trans. Image Process. 16(1), 22–35 (2007)

  33. 33.

    Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379–423 (1948)

  34. 34.

    Doyle, L., Mould, D.: Augmenting photographs with textures using the Laplacian pyramid. Vis. Comput. 35(10), 1489–1500 (2019)

  35. 35.

    Otsu, N.: A threshold selection method from gray-level histogram. IEEE Trans. Syst Man Cybern Syst. 9(1), 62–66 (1979)

  36. 36.

    Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sensors 18(4), 1064 (2018)

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Correspondence to Guohua Liu.

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Guohua Liu declares that he has no conflict of interest. Xiangtong Zheng declares that he has no conflict of interest.

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Liu, G., Zheng, X. Fabric defect detection based on information entropy and frequency domain saliency. Vis Comput (2020).

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  • Fabric defect detection
  • Information entropy
  • Visual attention
  • Hypercomplex Fourier transform