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Wire rope defect identification based on ISCM-LBP and GLCM features

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Abstract

The traditional local binary pattern (LBP) is susceptible to the influence of the centre pixel and noise and cannot accurately identify wire rope surface defects. To solve this problem, an image segmentation-based central multiscale local binary pattern (ISCM-LBP) and grey level cooccurrence matrix (GLCM) feature fusion method is proposed in this paper for defect recognition. Image segmentation and multiple scales are introduced into the local binary pattern algorithm to improve the image detail description and suppress noise sensitivity. Second, the centre pixel is connected with the neighbourhood pixel to enhance the robustness of the centre pixel. To further improve the image integrity description, PCA dimensionality reduction and GLCM feature fusion are performed on the features extracted by the ISCM-LBP algorithm, and the steel wire rope surface defects are identified by a support vector machine classifier. Experimental results show that the overall recognition rate reaches 97.5%, which is at least 5% higher than that of other algorithms and can effectively identify various defects on the surface of wire rope.

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Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Yuan, F., Hu, B., Liang, Zhou, Z.: Research Status and Prospect of Defect detection methods for steel wire Rope in service. Mach. Des. Manuf. 02, 260–262 (2010)

  2. Xin, Z., et al.: Study on clutter of electromagnetic detection signal of mine hoist wire rope. Coal Eng. 50(08), 119–121 (2018)

    Google Scholar 

  3. Xiaohua, J.: Research on non-destructive flaw detection system of wire rope core based on X-ray. Autom. Ind. Mine 40(08), 110–112 (2014)

    Google Scholar 

  4. Yuan, H., Bin, L., Zhou, Z.: Research status and prospect of defect detection methods for steel wire rope in service. Mach. Des. Manuf. 02, 260–262 (2010)

    Google Scholar 

  5. Yuan, F., Hu, B.-L., Zhou, Z.-J.: Research on the strength testing method of steel wire rope based on acoustic-ultrasonic technology. Mach. Des. Manuf. 07, 104–105 (2010)

    Google Scholar 

  6. Yonglei, D., et al.: Defect detection method of elevator wire rope based on machine vision. China Elev. 29(07), 10–12 (2018)

    Google Scholar 

  7. Zhou, P., et al.: A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes. Appl. Sci. 9(13), 2771 (2019)

    Article  Google Scholar 

  8. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)

    Article  Google Scholar 

  9. Li, L., et al.: Research progress and prospect of image texture classification methods. Acta Autom. Sin. 44(04), 584–607 (2018)

    Google Scholar 

  10. Zhao, J.: Detection of broken wire of Oil well wire rope based on image processing, Central South University (2014)

  11. Jiang, H.: Research on surface defect detection method of steel wire rope based on IWOA-SVM, Taiyuan University of Science and Technology (2021)

  12. Jiexian, H., et al.: Research on identification of corrosion and wear defects of steel wire rope. Surf. Technol. 45(10), 187–192 (2016)

    Google Scholar 

  13. Zhang, G.Y., et al.: Steel wire rope surface defect detection based on segmentation template and spatiotemporal gray sample set. Sensors 21(16), 5401 (2021)

    Article  Google Scholar 

  14. Zhihuai, L., et al.: Quantitative detection method of broken wire rope based on Principal Component analysis and BP neural network. J. Vib. Shock 37(18), 271–276 (2018)

    Google Scholar 

  15. Ruochen, D., et al.: Research on steel wire rope defect detection method based on Otsu segmentation and edge detection. Adv. Lasers Optoelectron. 58(16), 566–573 (2021)

    Google Scholar 

  16. Li, Z.: Intelligent elevator operation health evaluation and safety monitoring based on machine learning, Zhejiang University (2021)

  17. Zhou, P., et al.: A hybrid data-driven method for wire rope surface defect detection. IEEE Sens. J. 20(15), 8297–8306 (2020)

    Article  Google Scholar 

  18. Wu, X.S., Sun, J.D.: Joint-scale LBP: a new feature descriptor for texture classification. Vis. Comput. 33(3), 317–329 (2017)

    Article  Google Scholar 

  19. Li, L., Jun, K.: Overview of image texture feature extraction methods. J. Image Gr. 14(04), 622–635 (2009)

    Google Scholar 

  20. Liao, S.: Patterns, learning multi-scale block local binary, for face recognition. Springer, Berlin Heidelberg, Heidelberg (2007)

    Book  Google Scholar 

  21. Liu, Y.: Research and application of online detection algorithm for surface defects of cold rolled strip. University of Science and Technology, Beijing (2020)

    Google Scholar 

  22. Pan, Z., et al.: Adaptive center pixel selection strategy to Local Binary Pattern for texture classification. Expert Syst. Appl. 180(4), 115123 (2021)

    Article  Google Scholar 

  23. Haralick, R.M.: Textural features for image classification. IEEE Trans. Syst Man Cybern. SMC 3, 610–621 (1973)

    Article  Google Scholar 

  24. Kabbai, L., Abdellaoui, M., Douik, A.: Image classification by combining local and global features. Vis. Comput. 35(5), 679–693 (2019)

    Article  Google Scholar 

  25. Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. Springer, Berlin, Heidelberg (2004)

    Book  Google Scholar 

  26. Pietikäinen, M., Ojala, T., Xu, Z.: Rotation-invariant texture classification using feature istributions. Pattern Recognit. 33(1), 43–52 (2000)

    Article  Google Scholar 

  27. Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns. Springer, Berlin, Heidelberg (2000)

    Book  Google Scholar 

  28. Tan, X.Y., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

  29. Jabid, T., Kabir, M.H., Chae, O.: Gender classification using local directional pattern (LDP). In: 2010 20th International Conference on Pattern Recognition (2010)

  30. Chakraborti, T., et al.: LOOP descriptor: local optimal-oriented pattern. IEEE Signal Process. Lett. 25(5), 635–639 (2018)

    Article  Google Scholar 

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Funding

This study was partially supported by the National Natural Science Foundation of China (No.U1804147), Innovative Scientists and Technicians Team of Henan Provincial High Education (20IRTSTHN019), Science and Technology Project of Henan Province (No.212102210508 & No.212102210005).

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Authors and Affiliations

  1. Henan International Joint Laboratory of Direct Drive and Control of Intelligent Equipment Jiaozuo, School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, China

    Qunpo Liu & Xuhui Bu

  2. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, China

    Yang Song & Qi Tang

  3. College of Information and Systems, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran, Hokkaido, 050-8585, Japan

    Naohiko Hanajima

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  1. Qunpo Liu
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  2. Yang Song
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Correspondence to Yang Song.

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Author Qunpo Liu declares no conflict of interest, author Yang Song declares no conflict of interest, author Qi Tang declares no conflict of interest, author Xuhui Bu declares no conflict of interest and author Naohiko Hanajimaha declares no conflict of interest.

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Liu, Q., Song, Y., Tang, Q. et al. Wire rope defect identification based on ISCM-LBP and GLCM features. Vis Comput 40, 545–557 (2024). https://doi.org/10.1007/s00371-023-02800-6

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