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Tube Defect Detection Algorithm Under Noisy Environment Using Feature Vector and Neural Networks

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Abstract

Surface flaw detection has been advanced steadily for decades thank to the advent of computer vision and artificial intelligence. However, there exist serious defect detection challenges in tube manufacturing, including the lack of a collected dataset, decision-making ambiguity in engineering judgment, and unstable lighting condition of the environment. This work aims to investigate an effective method to distinguish deformity that performs despite these challenges to deliver quality control in tube manufacturing. We present a new tube detection algorithm under limited data set and noisy environment due to unstable lighting condition, for which we introduced a feature vector to describe the defect problem. Using the feature vector and a neural network we are able to successfully detect and classify tube defect.

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References

  1. Neogi, N., Mohanta, D. K., & Dutta, P. K. (2014). Review of vision-based steel surface inspection systems. Journal on Image and Video Processing, 2014, 50.

    Article  Google Scholar 

  2. Nakazawa, T., & Samara, A. (2014). Three-dimensional inline inspection for substrate warpage and ball grid array coplanarity using stereo vision. Applied Optics, 53, 3101–3109.

    Article  Google Scholar 

  3. Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2013). Scalable object detection using deep neural networks. arXiv:1312.2249.

  4. Xie, X. (2008). A review of recent advances in surface defect detection using texture analysis techniques. ELCVIA Electronic Letters on Computer Vision and Image Analysis, 7, 1–22.

    Article  Google Scholar 

  5. Luiz, A. O. M., Flavio, L. C. P., & Paulo, E. M. A. (2010). Automatic detection of surface defects on rolled steel using computer vision and artificial neural networks. In IEEE (pp. 1081–1086). https://doi.org/10.1109/iecon.2010.5675519.

  6. Yun, J. P., Jeon, Y.-J., Choi, D., & Kim, S. W. (2012). Real-time defect detection of steel wire rods using wavelet filters optimized by univariate dynamic encoding algorithm for searches. Journal of the Optical Society of America A, 29, 797.

    Article  Google Scholar 

  7. Choi, D.-C., Jeon, Y.-J., Lee, S. J., Yun, J. P., & Kim, S. W. (2014). Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method. Applied Optics, 53, 4865.

    Article  Google Scholar 

  8. Kuo, C.-F., Hsu, C.-T. M., Fang, C.-H., Chao, S.-M., & Lin, Y.-D. (2013). Automatic defect inspection system of colour filters using Taguchi-based neural network. International Journal of Production Research, 51, 1464–1476.

    Article  Google Scholar 

  9. Jeon, Y.-J., Choi, D., Lee, S. J., Yun, J. P., & Kim, S. W. (2016). Steel-surface defect detection using a switching-lighting scheme. Applied Optics, 55, 47.

    Article  Google Scholar 

  10. Xi, J., Shentu, L., Hu, J., & Li, M. (2017). Automated surface inspection for steel products using computer vision approach. Applied Optics, 56, 184.

    Article  Google Scholar 

  11. A practical guide to machine vision lighting: National instruments. http://www.ni.com/white-paper/6901/en/. Accessed May 5, 2018.

  12. Lee, D. J., Redd, S., Schoenberger, R., Xu, X., & Zhan, P. (2003). An automated fish species classification and migration monitoring system. In The 29th annual conference of the IEEE industrial electronics society, 2003. IECON’03 (Vol. 2, pp. 1080–1085).

  13. Yang, S., Zhang, C., & Wu, W. (2018). Binary output layer of feedforward neural networks for solving multi-class classification problems. arXiv:1801.07599[cs, math].

  14. Liu, Y., & Yu, F. (2014). Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision. Optics and Lasers in Engineering, 55, 243–257.

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by the 2016 Research Fund of University of Ulsan.

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Authors and Affiliations

  1. School of Mechanic and Automotive Engineering, University of Ulsan, Daehak-ro 93, Nam-gu, Ulsan, 44610, South Korea

    Chi-Tho Cao, Van-Phu Do & Byung-Ryong Lee

  2. Abeosystem Co, LTD, Daehak-ro 93, Nam-gu, Ulsan, 44610, South Korea

    Van-Phu Do & Byung-Ryong Lee

Authors
  1. Chi-Tho Cao
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  2. Van-Phu Do
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  3. Byung-Ryong Lee
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Correspondence to Byung-Ryong Lee.

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Cao, CT., Do, VP. & Lee, BR. Tube Defect Detection Algorithm Under Noisy Environment Using Feature Vector and Neural Networks. Int. J. Precis. Eng. Manuf. 20, 559–568 (2019). https://doi.org/10.1007/s12541-019-00023-1

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  • DOI: https://doi.org/10.1007/s12541-019-00023-1

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