A vision-based system for robotic inspection of marine vessels

  • Rosalia Maglietta
  • Annalisa Milella
  • Massimo Caccia
  • Gabriele Bruzzone
Original Paper
  • 68 Downloads

Abstract

This paper presents a novel intelligent system for the automatic visual inspection of vessels consisting of three processing levels: (a) data acquisition: images are collected using a magnetic climbing robot equipped with a low-cost monocular camera for hull inspection; (b) feature extraction: all the images are characterized by 12 features consisting of color moments in each channel of the HSV space; (c) classification: a novel tool, based on an ensemble of classifiers, is proposed to classify sub-images as rust or non-rust. This paper provides a helpful roadmap to guide future research on the detection of rusting of metals using image processing.

Keywords

Image processing Segmentation Classification 

Notes

Acknowledgements

This work has been funded by the EC FP7 SST.2008.5.2.1 Innovative Product Concepts project: Marine INspection rObotic Assistant System, Grant No. SCP8-GA- 2009-233715-MINOAS, and by the BANDIERA (Best Action for National Development of International Expert Researchers Activities) project RITMARE. The authors thank Giorgio Bruzzone, Edoardo Spirandelli and Mauro Giacopelli for their extraordinary contribution to the design, development and test of the MARC platform, and Arturo Argentieri for technical assistance. Special thanks to the MINOAS consortium and, in particular, to Albena Todorova and all the personnel of Dolphin shipyards who supported with their high professionalism and kindness the field validation of the system.

References

  1. 1.
    Acosta, M., Diaz, J., Castro, N.S.: An innovative image-processing model for rust detection using perlin noise to simulate oxide textures. Corros. Sci. 88, 141–151 (2014)CrossRefGoogle Scholar
  2. 2.
    Ancona, N., Maglietta, R., Piepoli, A., D’Addabbo, A., Cotugno, R., Savino, M., Liuni, S., Carella, M., Pesole, G., Perri, F.: On the statistical assessment of classifiers using dna microarray data. BMC Bioinform. 7, 38 (2006)CrossRefGoogle Scholar
  3. 3.
    Ancona, N., Maglietta, R., Stella, E.: Data representations and generalization error in kernel based learning machines. Pattern Recogn. 39(9), 1588–1603 (2006)CrossRefMATHGoogle Scholar
  4. 4.
    Avas, S., Ekinci, M.: Random forest-based tuberculosis bacteria classification in images of ZN-stained sputum smear samples. SIViP 8(1), 49–61 (2014)CrossRefGoogle Scholar
  5. 5.
    Banfield, R., Hall, L., Bowyer, K., Kegelmeyer, W.: Ensemble diversity measures and their application to thinning. Inf. Fusion 6(1), 49–62 (2005)CrossRefGoogle Scholar
  6. 6.
    Bibuli, M., Bruzzone, G., Bruzzone, G., Caccia, M., Giacopelli, M., Petitti, A., Spirandelli, E.: MARC: magnetic autonomous robotic crawler development and exploitation in the MINOAS project. In: Conference on Computer Applications and Information Technology in Maritime Industries (COMPIT), Liegi (Belgio), pp. 62–75 (2012)Google Scholar
  7. 7.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. J. Inf. Fusion 6(1), 5–20 (2005)CrossRefGoogle Scholar
  9. 9.
    Caccia, M., Robino, R., Bateman, W., Eich, M., Ortiz, A., Drikos, L., Todorova, A., Gaviotis, I., Spadoni, F., Apostolopoulou, V.: MINOAS—a Marine INspection rObotic Assistant: system requirements and design. In: Proceedings of IAV 2010, 7th IFAC Symposium on Intelligent Autonomous Vehicles (2010)Google Scholar
  10. 10.
    Ceamanos, X., Waske, B., Benediktsson, J., Chanussot, J., Fauvel, M., Sveinsson, J.: A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data. Int. J. Image Data Fusion 1, 293–307 (2010)CrossRefGoogle Scholar
  11. 11.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967)CrossRefMATHGoogle Scholar
  12. 12.
    Dietterich, T.: Machine-learning research: four current directions. AI Mag. 18(4), 97–136 (1997)Google Scholar
  13. 13.
    Du, P., Xia, J., Zang, W., Tan, K., Liu, Y., Liu, S.: Multiple classifier system for remote sensing image classification: a review. Sensor 12(4), 4764–4792 (2012)CrossRefGoogle Scholar
  14. 14.
    Eich, M., Bonnin-Pascual, F., Garcia-Fidalgo, E., Ortiz, A., Bruzzone, G., Koveos, Y., Kirchner, F.: A robot application for marine vessel inspection. J. Field Robot. 31(2), 319–341 (2014)CrossRefGoogle Scholar
  15. 15.
    Guo, Y., Hastie, T., Tibshirani, R.: Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8(1), 86–100 (2007)CrossRefMATHGoogle Scholar
  16. 16.
    Gupta, M., Rajagopalan, V., Rao, B.: Volumetric analysis of MR images for glioma classification and their effect on brain tissues. SIViP 11(7), 1337–1345 (2017)CrossRefGoogle Scholar
  17. 17.
    Hansen, L., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)CrossRefGoogle Scholar
  18. 18.
    Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York (2008)MATHGoogle Scholar
  19. 19.
    Hattori, K., Takahashi, M.: A new nearest-neighbor rule in the pattern classification problem. Pattern Recogn. 32(3), 425–432 (1999)CrossRefGoogle Scholar
  20. 20.
    Jackowski, K., Krawczyk, B., Woniak, M.: Cost-sensitive splitting and selection method for medical decision support system. Intell. Data Eng. Autom. Learn. IDEAL 7435, 850–857 (2012)Google Scholar
  21. 21.
    Kim, H., Pang, S., Je, H., Kim, D., Bang, S.: Constructing support vector machine ensemble. Pattern Recogn. 36(12), 2757–2767 (2003)CrossRefMATHGoogle Scholar
  22. 22.
    Kuncheva, L., Whitaker, C.: Measures of diversity in classifier ensembles. Mach. Learn. 51, 181–207 (2003)Google Scholar
  23. 23.
    Maglietta, R., Amoroso, N., Boccardi, M., Bruno, S., Chincarini, A., Frisoni, G., Inglese, P., Redolfi, A., Tangaro, S., Tateo, A., Bellotti, R.: Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm. Pattern Anal. Appl. 19(2), 579–591 (2016)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Medeiros, F., Ramalho, G., Bento, M., Medeiros, L.: On the evaluation of texture and color features for nondestructive corrosion detection. EURASIP J. Adv. Signal Process. (2010). doi: 10.1155/2010/817473 Google Scholar
  25. 25.
    Nemmour, H., Chibani, Y.: Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS J. Photogramm. Remote Sens. 61, 125–133 (2006)CrossRefGoogle Scholar
  26. 26.
    Patridge, D., Krzanowski, W.: Software diversity: practical statistics for its measurement and exploitation. Inf. Softw. Technol. 39, 707–717 (1997)CrossRefGoogle Scholar
  27. 27.
    Ruta, D., Gabrys, B.: Application of the evolutionary algorithms for classifier selection in multiple classifier systems with majority voting. In: Multiple Classifier Systems: Second International Workshop, MCS 2001 Cambridge, UK, July 2–4, 2001 Proceedings, pp. 399–408. Springer, Berlin (2001)Google Scholar
  28. 28.
    Salem, Y., Nasri, S.: Automatic recognition of woven fabrics based on texture and using SVM. SIViP 4(4), 429–434 (2010)CrossRefMATHGoogle Scholar
  29. 29.
    Vapnik, V.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Institute of Intelligent Systems for AutomationNational Research CouncilBariItaly
  2. 2.Institute of Intelligent Systems for AutomationNational Research CouncilGenoaItaly

Personalised recommendations