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Year 2024, Volume: 31 Issue: 133, 1 - 7, 31.03.2024
https://doi.org/10.7216/teksmuh.1459876

Abstract

Project Number

5220100

References

  • 1. Chen, J., Benesty, J., Huang, Y., & Doclo, S. (2006). New insights into the noise reduction Wiener filter. IEEE Transactions on audio, speech, and language processing, 14(4), 1218-1234.
  • 2. Chen, Z., Xu, W., Leng, W., & Fu, Y. (2010). A new high-speed foreign fiber detection system with machine vision. Mathematical Problems in Engineering, 2010.
  • 3. Du, Y. H., Li, X., Ren, W., & Zuo, H. (2023). Application of near-infrared spectroscopy and CNN-TCN for the identification of foreign fibers in cotton layers. Journal of Natural Fibers, 20(1), 2172638.
  • 4. Gonzales, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB. Pearson Prentice Hall.
  • 5. Ji, R., Li, D., Chen, L., & Yang, W. (2010). Classification and identification of foreign fibers in cotton on the basis of a support vector machine. Mathematical and Computer Modelling, 51(11-12), 1433-1437.
  • 6. Li, D., Yang, W., & Wang, S. (2010). Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine. Computers and electronics in agriculture, 74(2), 274-279.
  • 7. Shofner, F. M., & Williams, G. F. (1986). Evolution of the microdust and trash monitor for cotton classification. Textile Research Journal, 56(2), 150-156.
  • 8. Yuhong, D., Ting, M., Chengwu, Y., & Xiuming, J. (2017). Detection clustering analysis algorithm and system parameters study of the near-point multi-class foreign fiber. The Journal of the Textile Institute, 108(6), 1022-1027.
  • 9. Zhang, C., Li, T., & Zhang, W. (2021). The detection of impurity content in machine-picked seed cotton based on image processing and improved YOLO V4. Agronomy, 12(1), 66.
  • 10. Zhao, X., Guo, X., Luo, J., & Tan, X. (2018). Efficient detection method for foreign fibers in cotton. Information Processing in Agriculture, 5(3), 320-328.
  • 11. Wei, W., Deng, D., Zeng, L., Zhang, C., & Shi, W. (2019). Classification of foreign fibers using deep learning and its implementation on embedded system. International Journal of Advanced Robotic Systems, 16(4), 1729881419867600.
  • 12. Wei, W., Zhang, C., & Deng, D. (2020). Content estimation of foreign fibers in cotton based on deep learning. Electronics, 9(11), 1795.
  • 13. Xiaoyun, H., P. Wei, L. Zhang, B. Deng, Y. Pan, and S. Zhenwei. (2018). Detection method of foreign fibers in seed cotton based on deep-learning. Fangzhi Xuebao/Journal of Textile Research 39 (6):131–35. doi:10.13475/j.fzxb.20170803105.

IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE

Year 2024, Volume: 31 Issue: 133, 1 - 7, 31.03.2024
https://doi.org/10.7216/teksmuh.1459876

Abstract

The image processing philosophy is mainly determined by the complexity of the image and provides the necessary information to be derived from the image. In the textile industry, the image processing technique focuses on the determination of the geometric properties of the fibers such as cross-sectional shape, diameter, length, fineness, and curl while the studies on the yarn characteristics mostly focus on the determination of yarn hairiness, yarn unevenness and yarn defects (thick place, thin place and neps). In this study, previous studies about image processing approaches that are applied for fiber characteristics were investigated. A case study was conducted to automatically determine the visible foreign matter in the waste cotton bundle that can be used for recycled cotton yarn production. It was revealed that the image processing methods can be successfully applied for foreign fiber and matter detection in cotton bundle. As a result, it is emphasized that the waste cotton properties can be specified with a sensitive and accurate approach via image processing technique, objective and numerical determination can be obtained instead of visual evaluation based on experience.

Supporting Institution

Scientific and Technological Research Council of Turkey (TUBİTAK)

Project Number

5220100

Thanks

This study is supported by the Scientific and Technological Research Council of Turkey (TUBİTAK). Project Number: 5220100. We express our sincere thanks for their financial support.

References

  • 1. Chen, J., Benesty, J., Huang, Y., & Doclo, S. (2006). New insights into the noise reduction Wiener filter. IEEE Transactions on audio, speech, and language processing, 14(4), 1218-1234.
  • 2. Chen, Z., Xu, W., Leng, W., & Fu, Y. (2010). A new high-speed foreign fiber detection system with machine vision. Mathematical Problems in Engineering, 2010.
  • 3. Du, Y. H., Li, X., Ren, W., & Zuo, H. (2023). Application of near-infrared spectroscopy and CNN-TCN for the identification of foreign fibers in cotton layers. Journal of Natural Fibers, 20(1), 2172638.
  • 4. Gonzales, R. C., Woods, R. E., & Eddins, S. L. (2004). Digital image processing using MATLAB. Pearson Prentice Hall.
  • 5. Ji, R., Li, D., Chen, L., & Yang, W. (2010). Classification and identification of foreign fibers in cotton on the basis of a support vector machine. Mathematical and Computer Modelling, 51(11-12), 1433-1437.
  • 6. Li, D., Yang, W., & Wang, S. (2010). Classification of foreign fibers in cotton lint using machine vision and multi-class support vector machine. Computers and electronics in agriculture, 74(2), 274-279.
  • 7. Shofner, F. M., & Williams, G. F. (1986). Evolution of the microdust and trash monitor for cotton classification. Textile Research Journal, 56(2), 150-156.
  • 8. Yuhong, D., Ting, M., Chengwu, Y., & Xiuming, J. (2017). Detection clustering analysis algorithm and system parameters study of the near-point multi-class foreign fiber. The Journal of the Textile Institute, 108(6), 1022-1027.
  • 9. Zhang, C., Li, T., & Zhang, W. (2021). The detection of impurity content in machine-picked seed cotton based on image processing and improved YOLO V4. Agronomy, 12(1), 66.
  • 10. Zhao, X., Guo, X., Luo, J., & Tan, X. (2018). Efficient detection method for foreign fibers in cotton. Information Processing in Agriculture, 5(3), 320-328.
  • 11. Wei, W., Deng, D., Zeng, L., Zhang, C., & Shi, W. (2019). Classification of foreign fibers using deep learning and its implementation on embedded system. International Journal of Advanced Robotic Systems, 16(4), 1729881419867600.
  • 12. Wei, W., Zhang, C., & Deng, D. (2020). Content estimation of foreign fibers in cotton based on deep learning. Electronics, 9(11), 1795.
  • 13. Xiaoyun, H., P. Wei, L. Zhang, B. Deng, Y. Pan, and S. Zhenwei. (2018). Detection method of foreign fibers in seed cotton based on deep-learning. Fangzhi Xuebao/Journal of Textile Research 39 (6):131–35. doi:10.13475/j.fzxb.20170803105.
There are 13 citations in total.

Details

Primary Language English
Subjects Textile Technology
Journal Section Articles
Authors

Elif Gültekin

Halil İbrahim Çelik

Hatice Kübra Kaynak

S. Büşra Zorlu This is me

Mehmet Kertmen

Faruk Mert

Project Number 5220100
Publication Date March 31, 2024
Submission Date November 24, 2023
Acceptance Date March 18, 2024
Published in Issue Year 2024 Volume: 31 Issue: 133

Cite

APA Gültekin, E., Çelik, H. İ., Kaynak, H. K., Zorlu, S. B., et al. (2024). IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE. Tekstil Ve Mühendis, 31(133), 1-7. https://doi.org/10.7216/teksmuh.1459876
AMA Gültekin E, Çelik Hİ, Kaynak HK, Zorlu SB, Kertmen M, Mert F. IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE. Tekstil ve Mühendis. March 2024;31(133):1-7. doi:10.7216/teksmuh.1459876
Chicago Gültekin, Elif, Halil İbrahim Çelik, Hatice Kübra Kaynak, S. Büşra Zorlu, Mehmet Kertmen, and Faruk Mert. “IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE”. Tekstil Ve Mühendis 31, no. 133 (March 2024): 1-7. https://doi.org/10.7216/teksmuh.1459876.
EndNote Gültekin E, Çelik Hİ, Kaynak HK, Zorlu SB, Kertmen M, Mert F (March 1, 2024) IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE. Tekstil ve Mühendis 31 133 1–7.
IEEE E. Gültekin, H. İ. Çelik, H. K. Kaynak, S. B. Zorlu, M. Kertmen, and F. Mert, “IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE”, Tekstil ve Mühendis, vol. 31, no. 133, pp. 1–7, 2024, doi: 10.7216/teksmuh.1459876.
ISNAD Gültekin, Elif et al. “IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE”. Tekstil ve Mühendis 31/133 (March 2024), 1-7. https://doi.org/10.7216/teksmuh.1459876.
JAMA Gültekin E, Çelik Hİ, Kaynak HK, Zorlu SB, Kertmen M, Mert F. IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE. Tekstil ve Mühendis. 2024;31:1–7.
MLA Gültekin, Elif et al. “IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE”. Tekstil Ve Mühendis, vol. 31, no. 133, 2024, pp. 1-7, doi:10.7216/teksmuh.1459876.
Vancouver Gültekin E, Çelik Hİ, Kaynak HK, Zorlu SB, Kertmen M, Mert F. IMAGE PROCESSING APPROACH FOR FOREIGN MATERIAL DETECTION IN COTTON BUNDLE. Tekstil ve Mühendis. 2024;31(133):1-7.