Machine Vision and Applications

, Volume 28, Issue 5–6, pp 591–605 | Cite as

Automatic inspection of aeronautic components

  • Marco San Biagio
  • Carlos Beltrán-González
  • Salvatore Giunta
  • Alessio Del Bue
  • Vittorio Murino
Original Paper

Abstract

Industrial processes are costly in terms of time, money and customer satisfaction. The global economic pressures have gradually led businesses to improve these processes to become more competitive. As a result, the demand of intelligent visual inspection systems aimed at ensuring the high quality in production lines is increasing. In this paper, we present a computer vision system that, using only images, is able to address two main problems: (i) model checking: automatically check whether a component meets given specifications or rules, (ii) visual inspection: defect inspection on irregular surfaces, in particular, decolourization and scratches detection. In the experimental results, we show the effectiveness of our system and the readiness of such technologies for their integration in industrial processes.

Keywords

Automatic visual inspection Model checking Machine learning Defects inspection Image processing Machine vision Registration Multi-view analysis 

References

  1. 1.
    Aiger, D., Talbot, H.: The phase only transform for unsupervised surface defect detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 295–302 (2010)Google Scholar
  2. 2.
    Alarcón-Herrera, J., Xiang, C., Xuebo, Z.: Viewpoint selection for vision systems in industrial inspection. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 4934 – 4939 (2014)Google Scholar
  3. 3.
    Bahlmann, C., Heidemann, G., Ritter, H.: Artificial neural networks for automated quality control of textile seams. Pattern Recognit. 32(1), 1049–1060 (1999)CrossRefGoogle Scholar
  4. 4.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM, New York, NY, USA (1992)Google Scholar
  5. 5.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with OpenCV Library, 1st edn. O’Reilly Media, Beijing (2008)Google Scholar
  6. 6.
    Caulier, Y., Bourennane, S.: An image content description technique for the inspection of specular objects. EURASIP J. Adv. Signal Process. 2008, 195263 (2008)CrossRefMATHGoogle Scholar
  7. 7.
    Chin, R.: Automated visual inspection: 1981 to 1987. Comput. Vis. Gr. Image Process. 41(3), 346–381 (1988)CrossRefGoogle Scholar
  8. 8.
    Chin, R., Harlow, C.: Automated visual inspection: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 4(6), 557–573 (1982)CrossRefGoogle Scholar
  9. 9.
    Choi, J., Kim, C.: Unsupervised detection of surface defects: A two-step approach. In: 2012 19th IEEE International Conference on Image Processing, pp. 1037–1040 (2012)Google Scholar
  10. 10.
    Corke, P.I.: Robotics, Vision and Control: Fundamental Algorithms in Matlab. Springer, Berlin, Heidelberg (2011)CrossRefMATHGoogle Scholar
  11. 11.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)MATHGoogle Scholar
  12. 12.
    Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The Pascal visual object classes VOC challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  13. 13.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28, 2000 (1998)MathSciNetMATHGoogle Scholar
  14. 14.
    Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit. 47(6), 2280–2292 (2014). doi:10.1016/j.patcog.2014.01.005
  15. 15.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, New York (2003)MATHGoogle Scholar
  16. 16.
    Kumar, A.: Computer-vision-based fabric defect detection: A survey. IEEE Trans. Ind. Electron. 55(1), 348–363 (2008)CrossRefGoogle Scholar
  17. 17.
    Legland, D.: Matgeom: matlab geometry toolbox for 2d/3d geometric computing. https://github.com/dlegland/matGeom (2009)
  18. 18.
    Lepetit, V., Moreno-Noguer, F., Fua, P.: Epnp: an accurate o(n) solution to the pnp problem. Int. J. Comput. Vis. 81(2), 155–166 (2008)CrossRefGoogle Scholar
  19. 19.
    Levenberg, K.: A method for the solution of certain non-linear problems in least squares. Q. Appl. Math. 2, 164–168 (1944)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Malamas, E., Petrakis, E., Zervakis, M., Petit, L., Legat, J.D.: A survey on industrial vision systems, applications and tools, image and vision computing 21. Image Vis. Comput. 21, 171–188 (2003)CrossRefGoogle Scholar
  21. 21.
    Markou, M., Singh, S.: Novelty detection: a review—part 2: neural network-based approaches. Signal Process. 83(12), 2499–2521 (2003)CrossRefMATHGoogle Scholar
  22. 22.
    Marquardt, D.W.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11(2), 431–441 (1963)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Moganti, M., Ercal, F., Dagli, C., Tsunekawa, S.: Automatic PCB inspection algorithms: a survey. Comput. Vis. Image Underst. (CVIU) 63(2), 287–313 (1996)CrossRefGoogle Scholar
  24. 24.
    Newman, T., Jain, A.: A survey of automated visual inspection. Comput Vis Image Underst. (CVIU) 61, 231–262 (1995)CrossRefGoogle Scholar
  25. 25.
    Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. Lett. 1(29), 51–59 (1998)Google Scholar
  26. 26.
    Ojala, T., Pietikinen, M., Menp, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Learn. (PAMI) 24(7), 971–987 (2002)CrossRefGoogle Scholar
  27. 27.
    Park, Y., Kweon, I.S.: Ambiguous surface defect image classification of amoled displays in smartphones. IEEE Trans. Ind. Inform. 99, 1–1 (2016)Google Scholar
  28. 28.
    Peng, X., Chen, Y., Yu, W., Zhou, Z., Sun, G.: An online defects inspection method for float glass fabrication based on machine vision. Int. J. Adv. Manuf. Technol. 39(11), 1180–1189 (2007)Google Scholar
  29. 29.
    Scott, W.R.: Model-based view planning. Mach. Vis. Appl. 20(1), 47–69 (2009)CrossRefMATHGoogle Scholar
  30. 30.
    Sturm, P.F., Maybank, S.J.: On plane-based camera calibration: A general algorithm, singularities, applications. In: 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, p. 437 vol. 1 (1999)Google Scholar
  31. 31.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, New York (2010)MATHGoogle Scholar
  32. 32.
    Thomas, A., Rodd, M., Holt, J., Neill, C.: Real-time industrial visual inspection: a review. Real Time Imaging 1(2), 139–158 (1995)CrossRefGoogle Scholar
  33. 33.
    Torres, F., Sebastian, J., Aracil, R., Jimenez, L., Reinoso, O.: Automated real-time visual inspection system for high-resolution superimposed printings. Image Vis. Comput. 16(1213), 947–958 (1998)CrossRefGoogle Scholar
  34. 34.
    Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment—a modern synthesis. In: Proceedings of the International Workshop on Vision Algorithms: Theory and Practice. ICCV ’99, . Springer-Verlag, London, pp. 298–372 (2000)Google Scholar
  35. 35.
    Tucker, J.: Inside beverage can inspection: an application from start to finish. In: Proceedings of the Vision ’89 Conference (1989)Google Scholar
  36. 36.
    Viola, P., Jones, M.J., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. J. Comput. Vis. 63(2), 153–161 (2005)CrossRefGoogle Scholar
  37. 37.
    Xie, X.: A review of recent advances in surface defect detection using texture analysis techniques. Electron. Lett. Comput. Vis. Image Anal. 7(3), 1–22 (2008)Google Scholar
  38. 38.
    Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: ICCV, pp. 666–673 (1999)Google Scholar
  39. 39.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans.Pattern Anal. Mach. Intell. (PAMI) 22(11), 1330–1334 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Pattern Analysis and Computer Vision Department (PAVIS)Istituto Italiano di TecnologiaGenovaItaly
  2. 2.AVIOAeroRivalta di TorinoItaly

Personalised recommendations