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HCCD: Haar-Based Cascade Classifier for Crack Detection on a Propeller Blade

  • R. SaveethEmail author
  • S. Uma Maheswari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1045)

Abstract

Crack detection in aircraft components is an important assessment because even a small unnoticed crack tends to critical crack length. Aviation demands reliability, and therefore, periodical inspection of cracks in aircraft parts like engine turbine blade, aircraft skin, rivets, wing spar, bulk fuselage, and airwings has to be detected in a fixed interval, but it requires human effort and expert’s knowledge. Features are extracted using extended Haar-like features and it has been given as input to cascade classifier to classify cracks and non-cracks images of a propeller blade. The supervised learning algorithm is developed and trained by a set of positive and negative images. The experimental results validate the test images by the cascading classifier to locate cracks.

Keywords

Propeller blade Crack detection Cascading classifier Haar-like features Supervised learning 

References

  1. 1.
    Abdel-Qader, O.A., Kelly, M.E.: Analysis of edge detection techniques for crack identification in bridges. J. Comput. Civil Eng. 17(4), 255–263 (2003)CrossRefGoogle Scholar
  2. 2.
    Fujita, Y., Hamamoto, Y.: A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 22(2), 245–254 (2010)CrossRefGoogle Scholar
  3. 3.
    Jahanshahi, M.R., Masri, S.F.: Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures. Autom. Constr. 22, 567–576 (2012)CrossRefGoogle Scholar
  4. 4.
    Chen, F.-C., Jahanshahi, M.R., Wu, R.-T., Joffe, C.: A texture-based video processing methodology using Bayesian data fusion for autonomous crack detection on metallic surfaces. Comput.-Aided Civil Infrastruct. Eng. 32(4), 271–287 (2017)CrossRefGoogle Scholar
  5. 5.
    Zhang, L., Yang, F., Zhang, Y.D., Zhu, Y.J.: Road crack detection using deep convolutional neural network. In: Proceedings of the IEEE International Conference on Image Processing, pp. 3708–3712 (Sep 2016)Google Scholar
  6. 6.
    Meinlschmidt, P.: Wilhelm-klauditz-institut (wki), fraunhoferinstitute for wood research, braunsch weig. thermo-graphic detection of defects in wood and wood-based materials. In: 14th international Symposium of nondestructive testing of wood, Hannover, Germany, May 2–4 (2005)Google Scholar
  7. 7.
    Tong, X., Guo, J., Ling, Y., Yin, Z.: A new image-based method for concrete bridge bottom crack detection. In: 2011 International Conference on Image Analysis and Signal Processing, pp. 568–571 (2011)Google Scholar
  8. 8.
    Hutchinson, T.C., Chen, Z.: Improved image analysis for evaluating concrete damage. J. Comput. Civ. Eng. 20(3), 210–216 (2006)CrossRefGoogle Scholar
  9. 9.
    Yamaguchi, T., Nakamura, S., Hashimoto, S.: An efficient crack detection method using percolation-based image processing. In: 2008 3rd IEEE Conference on Industrial Electronics and Applications, pp. 1875–1880 (2008)Google Scholar
  10. 10.
    Choi, D.-C., Jeon, Y.-J., Lee, S.J., Yun, J.P., Kim, S.W.: Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method. Appl. Opt. 53(22), 4865–4872 (2014)CrossRefGoogle Scholar
  11. 11.
    Choudhary, G.K., Dey, S.: Crack detection in concrete surfaces using image processing, fuzzy logic, and neural networks. In: Fifth International Conference on Advanced Computational Intelligence (ICACI). IEEE, New York (2012)Google Scholar
  12. 12.
    Adhikari, R.S., Moselhi, O., Bagchi, A.: Image-based retrieval of concrete crack properties for bridge inspection. Autom. Constr. 39, 180–194 (2014)CrossRefGoogle Scholar
  13. 13.
    Yang, Y.-S., Yang, C.-M., Huang, C.-W.: Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis. Adv. Eng. Softw. 83, 99–108 (2015)CrossRefGoogle Scholar
  14. 14.
    Salman, M., Mathavan, S., Kamal, K., Rahman, M.: Pavement crack detection using the Gabor filter. In: Proceedings of 16th International IEEE Annual Conference on Intelligent Transportation Systems, pp. 2093–2044 (2013)Google Scholar
  15. 15.
    Fan, Y., Deng, Y., Zeng, Z., Udpa, L., Shih, W., Fitzpatrick, G.: Aging aircraft rivet site inspection using magneto-optic imaging: Automation and real-time image processing. In: 9th Joint FAA/DoD/NASA Aging Aircraft Conference (2006)Google Scholar
  16. 16.
    Xu, J., Liu, T., Yin, X.M., Wong, B.S., Hassan, S.B.: Automatic X-ray crack inspection for aircraft wing fastener holes. In: 2nd International Symposium on NDT in Aerospace 2010–Mo. 5. A. 4Google Scholar
  17. 17.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: Proceedings. International Conference on Image Processing, vol. 1, pp. I-900–I-903 (2002)Google Scholar
  18. 18.
    Chen, F.-C., Jahanshahi, M.R.: NB-CNN: Deep learning-based crack detection using convolutional neural network and Naïve Bayes data fusion. IEEE Trans. Ind. Electron. 65(5) (May 2018)Google Scholar
  19. 19.
    Iyer, S., Sinha, S.K.: A robust approach for automatic detection and segmentation of cracks in underground pipeline images. Image Vis. Comput. 23(10), 931–933 (2005)CrossRefGoogle Scholar
  20. 20.
    Qiao, W., Lu, D.: A survey on wind turbine condition monitoring and fault diagnosis—Part I: Components and subsystems. IEEE Trans. Ind. Electron. 62(10), 6536–6545 (2015)CrossRefGoogle Scholar
  21. 21.
    Wu, X.-Y., Xu, K., Xu, J.-W.: Application of undecimated wavelet transform to surface defect detection of hot rolled steel plates. Proc. Congr. Image Signal Process. 4, 528–532 (2008)Google Scholar
  22. 22.
    Neogi, N., Mohanta, D.K., Dutta, P.K.: Review of vision-based steel surface inspection systems. EURASIPJ. Image Video Process. 2014(1), 1–19 (2014)CrossRefGoogle Scholar
  23. 23.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-511–I-518 (2001)Google Scholar
  24. 24.
    Lee, J.-K., Park, J.-Y., Oh, K.-Y., Ju, S.-H., Lee, J.-S.: Transformation algorithm of wind turbine blade moment signals for blade condition monitoring. Renew. Energy 79, 209–218 (2015)CrossRefGoogle Scholar
  25. 25.
    Musolino, A., Raugi, M., Tucci, M., Turcu, F.: Feasibility of defect detection in concrete structures via ultrasonic investigation. In: Progress in Electromagnetics Research Symposium 2007, Prague, Czech Republic, August 27–30Google Scholar
  26. 26.
    Xu, W., Tang, Z., Zhou, J., Ding, J.: Pavement crack detection based on saliency and statistical features. In: 20th IEEE International Conference on Image Processing (ICIP), pp. 4093–4097 (2013)Google Scholar
  27. 27.
    Prasanna, P., Dana, K., Gucunski, N., Basily, B., La, H., Lim, R., Parvardeh, H.: Automated crack detection on concrete bridges. IEEE Trans. Autom. Sci. Eng. 13(2), 591–599 (2016)CrossRefGoogle Scholar
  28. 28.
    Fitzpatrick, G.L., et al.: Magneto-optic/eddy current imaging of subsurface corrosion and fatigue cracks in aging aircraft. Rev. Prog. Quant. Nondestr. Eval. 15 (1996)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia
  2. 2.Department of Electronics and Communication EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia

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