An Integrated Deep Neural Network for Defect Detection in Dynamic Textile Textures

  • Dirk Siegmund
  • Ashok Prajapati
  • Florian Kirchbuchner
  • Arjan Kuijper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11047)


This paper presents a comprehensive defect detection method for two common fabric defects groups. Most existing systems require textiles to be spread out in order to detect defects. This method can be applied when the textiles are not spread out and does not require any pre- processing. The deep learning architecture we present is based on transfer learning and localizes and recognizes cuts, holes and stain defects. Classification and localization is combined into a single system combining two different networks. The experiments this paper presents show that even without adding depth information, the network was able to distinguish between stain and shadow. This method has been successful even for textiles in voluminous shape and is less computationally intensive than other state-of-the-art methods.



This work was supported by the German Federal Ministry of Education and Research (BMBF) as well as by the Hessen State Ministry for Higher Education, Research and the Arts (HMWK) within CRISP.


  1. 1.
    Siegmund, D., Kaehm, O., Handtke, D.: Rapid classification of textile fabrics arranged in piles. In: Proceedings of the 13th International Joint Conference on e-Business and Telecommunications, pp. 99–105 (2016)Google Scholar
  2. 2.
    Borghese, N.A., Fomasi, M.: Automatic defect classification on a production line. Intell. Ind. Syst. 1, 373–393 (2015)CrossRefGoogle Scholar
  3. 3.
    Siegmund, D., Kuijper, A., Braun, A.: Stereo-image normalization of voluminous objects improves textile defect recognition. In: Bebis, G., et al. (eds.) ISVC 2016. LNCS, vol. 10072, pp. 181–192. Springer, Cham (2016). Scholar
  4. 4.
    Li, Y., Zhao, W., Pan, J.: Deformable patterned fabric defect detection with fisher criterion-based deep learning. IEEE Trans. Autom. Sci. Eng. 14, 1256–1264 (2017)CrossRefGoogle Scholar
  5. 5.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). Scholar
  6. 6.
    Siegmund, D., Samartzidis, T., Fu, B., Braun, A., Kuijper, A.: Fiber defect detection of inhomogeneous voluminous textiles. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds.) MCPR 2017. LNCS, vol. 10267, pp. 278–287. Springer, Cham (2017). Scholar
  7. 7.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)CrossRefGoogle Scholar
  8. 8.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  9. 9.
    Girshick, R.: Fast R-CNN. arXiv preprint arXiv:1504.08083 (2015)
  10. 10.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  11. 11.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  12. 12.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)Google Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  14. 14.
    Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111, 98–136 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dirk Siegmund
    • 1
    • 2
  • Ashok Prajapati
    • 1
    • 2
  • Florian Kirchbuchner
    • 1
    • 2
  • Arjan Kuijper
    • 1
    • 2
  1. 1.Fraunhofer Institute for Computer Graphics Research (IGD)DarmstadtGermany
  2. 2.Technische Universität DarmstadtDarmstadtGermany

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