Advertisement

Classification of Cotton and Flax Fiber Images Based on Inductive Transfer Learning

  • Yuhan Jiang
  • Song Cai
  • Chunyan ZengEmail author
  • Zhifeng Wang
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 97)

Abstract

Aiming at the existing problems of high labor cost, huge training data and long detection period for Identification technology of cotton flax fiber, which is based on textural feature and convolutional neural network (CNN) method. In this paper, it proposed a cotton and flax fiber detection method based on transfer learning. According to sharing the weight parameters of the convolutional layer and the pooling layer, the model hyperparameters can be adjusted for the new network to achieve high detection accuracy. The experimental results show that the detection accuracy of cotton flax fiber obtained by transfer learning is up to 97.3%, the sensitivity is 96.7%, and the specificity is 98.2%. Compared with traditional machines, transfer learning method have large increase in the three indicators. Furthermore, the transfer learning method has shorter training time and fewer data sets.

Notes

Acknowledgments

This research was supported by National Natural Science Foundation of China (No. 61901165, No. 61501199), Science and Technology Research Project of Hubei Education Department (No. Q20191406), Excellent Young and Middle-aged Science and Technology Innovation Team Project in Higher Education Institutions of Hubei Province (No. T201805), Hubei Natural Science Foundation (No. 2017CFB683), and self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (No. CCNU18QN021).

References

  1. 1.
    Jin, Y., Cheng, W.: Practice of identification of textile fibers with similar properties by infrared spectroscopy. J. China Fiber Inspection 519(11), 78–80 (2018)Google Scholar
  2. 2.
    Ying, L., et al.: Cotton/linen single fiber identification based on longitudinal longitudinal microscopic images. J. Text. J. 33(04), 12–18 (2012)Google Scholar
  3. 3.
    Wang, F., et al.: Cotton flax fiber identification technology based on texture features. J. Cotton Text. Technol. 44(04), 1–5 (2016)Google Scholar
  4. 4.
    Wang, Y., et al.: Automatic identification system for flax and cotton based on convolutional neural network. J. Text. Test. Stand. 4(06), 19–23 (2018)Google Scholar
  5. 5.
    Zhang, R., et al.: Automatic detection and classification of colorectal polyps by transferring low-level CNN features from non-medical domain. IEEE J. Biomed. Health Inform. 21(1), 41 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Vesal, S., et al.: Classification of Breast Cancer Histology Images Using Transfer Learning. Springer, Cham (2018)CrossRefGoogle Scholar
  7. 7.
    Shao, L., et al.: Transfer learning for visual categorization: a survey. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1019–1034 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  9. 9.
    Liu, X., et al.: Transfer learning with convolutional neural network for early gastric cancer classification on magnifiying narrow-band imaging images. In: 2018 25th IEEE International Conference on Image Processing (ICIP) (2018)Google Scholar
  10. 10.
    Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big Data 3(1), 9 (2016)CrossRefGoogle Scholar
  11. 11.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
  12. 12.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yuhan Jiang
    • 1
  • Song Cai
    • 1
  • Chunyan Zeng
    • 1
    Email author
  • Zhifeng Wang
    • 2
  1. 1.Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage SystemHubei University of TechnologyWuhanChina
  2. 2.Department of Digital Media TechnologyCentral China Normal UniversityWuhanChina

Personalised recommendations