Skip to main content
SpringerLink
Log in
Book cover

Robot 2015: Second Iberian Robotics Conference pp 223–234Cite as

Visual Inspection of Vessels by Means of a Micro-Aerial Vehicle: An Artificial Neural Network Approach for Corrosion Detection

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 418))

Abstract

Periodic visual inspection of the different surfaces of a vessel hull is typically performed by trained surveyors at great cost, both in time and in economical terms. Assisting them during the inspection process by means of mechanisms capable of automatic or semi-automatic defect detection would certainly decrease the inspection cost. This paper describes a defect detection approach comprising: (1) a Micro-Aerial Vehicle (MAV) which is used to collect images from the surfaces under inspection, particularly focusing on remote areas where the surveyor has no visual access; and (2) a coating breakdown/corrosion detector based on a 3-layer feed-forward artificial neural network. The success of the classification process depends not only on the defect detector but also on a number of assistance functions that are provided by the control architecture of the aerial platform, whose aim is to improve picture quality. Both aspects are described along the different sections of the paper, as well as the classification performance attained.

This work is partially supported by the European Social Fund through grant FPI11-43123621R (Conselleria d’Educacio, Cultura i Universitats, Govern de les Illes Balears) and by the EU FP7 project INCASS (GA 605200). This publication reflects only the authors’ views and the European Union is not liable for any use that may be made of the information contained therein. The authors also thank Fabricio Ardizon for his involvement in project INCASS.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amano, T.: Correlation based image defect detection. In: Proc. ICPR, pp. 163–166 (2006)

    Google Scholar 

  2. Arkin, R.C.: Behavior-based Robotics. MIT press (1998)

    Google Scholar 

  3. Bonnin-Pascual, F., Ortiz, A.: Combination of weak classifiers for metallic corrosion detection and guided crack location. In: IEEE Intl. Conf. Emerging Tech. & Factory Automat. (2010)

    Google Scholar 

  4. Bonnin-pascual, F., Ortiz, A.: A probabilistic approach for defect detection based on saliency mechanisms. In: IEEE Intl. Conf. Emerging Tech. & Factory Automat. (2014)

    Google Scholar 

  5. Bonnin-Pascual, F., Ortiz, A.: Corrosion Detection for Automated Visual Inspection. In: Aliofkhazraei, D.M. (ed.) Developments in Corrosion Protection, pp. 619–632. InTech (2014)

    Google Scholar 

  6. Boukouvalas, C., Kittler, J., Marik, R., Mirmehdi, M., Petrou, M.: Ceramic tile inspection for colour and structural defects. Technical Report CS-EXT-1995-052 (1995), also: Proc. AMPT, pp. 390–399 (1995)

    Google Scholar 

  7. Chang, C.-L., Chang, H.-H., Hsu, C.P.: An intelligent defect inspection technique for color filter. In: Pro. IEEE Intl. Conf. Mechatronics, pp. 933–936 (2005)

    Google Scholar 

  8. Castilho, H., Pinto, J., Limas, A.: An automated defect detection based on optimized thresholding. In: Proc. Intl. Conf. Image Analysis & Recognition, pp. 790–801 (2006)

    Google Scholar 

  9. Cheng, G., Zelinsky, A.: Supervised Autonomy: A Framework for Human-Robot Systems Development. Auton. Robot. 10, 251–266 (2001)

    Article  MATH  Google Scholar 

  10. Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley (2001)

    Google Scholar 

  11. Eich, M., Bonnin-Pascual, F., Garcia-Fidalgo, E., Ortiz, A., Bruzzone, G., Koveos, Y., Kirchner, F.: A Robot Application to Marine Vessel Inspection. J. Field Robot. 31(2), 319–341 (2014)

    Article  Google Scholar 

  12. Fujita, Y., Mitani, Y., Hamamoto, Y.: A method for crack detection on a concrete structure. In: Proc. ICPR, pp. III: 901–904 (2006)

    Google Scholar 

  13. Gurdan, D., Stumpf, J., Achtelik, M., Doth, K.M., Hirzinger, G., Rus, D.: Energy-efficient Autonomous Four-rotor Flying Robot Controlled at 1 kHz. In: Proc. IEEE ICRA, pp. 361–366 (2007)

    Google Scholar 

  14. Honegger, D., Meier, L., Tanskanen, P., Pollefeys, M.: An open source and open hardware embedded metric optical flow CMOS camera for indoor and outdoor applications. In: Proc. IEEE ICRA, pp. 1736–1741 (2013)

    Google Scholar 

  15. Hongbin, J., Murphey, Y., Jinajun, S., Tzyy-Shuh, C.: An intelligent real-time vision system for surface defect detection. In: Proc. ICPR, pp. III: 239–242 (2004)

    Google Scholar 

  16. Jahanshahi, M., Masri, S.: Effect of color space, color channels, and sub-image block size on the performance of wavelet-based texture analysis algorithms: An application to corrosion detection on steel structures. In: ASCE Intl. Workshop on Computing in Civil Eng. (2013)

    Google Scholar 

  17. Ji, G., Zhu, Y., Zhang, Y.: The corroded defect rating system of coating material based on computer vision. In: Trans. Edutainment VIII, LNCS, vol. 7220, pp. 210–220. Springer (2012)

    Google Scholar 

  18. Jiang, B.C., Wang, C.C., Chen, P.L.: Logistic regression tree applied to classify PCB golden finger defects. Intl. Jour. Advanced Manufacturing Technology 24(7–8), 496–502 (2004)

    Article  Google Scholar 

  19. Kumar, A., Shen, H.: Texture inspection for defects using neural networks and support vector machines. In: Proc. IEEE ICIP. vol. III, pp.353–356 (2002)

    Google Scholar 

  20. 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 

  21. Oullette, R., Browne, M., Hirasawa, K.: Genetic algorithm optimization of a convolutional neural network for autonomous crack detection. In: Proc. IEEE CEC, pp. I: 516–521 (2004)

    Google Scholar 

  22. Ruffo, M., Castro, M.D., Molinari, L., Losito, R., Masi, A., Kovermann, J., Rodrigues, L.: New Infrared Time-of-flight Measurement Sensor for Robotic Platforms. In: IMEKO TC4 Int. Symposium and Int. Workshop on ADC Modelling and Testing, pp. 13–18 (2014)

    Google Scholar 

  23. Siegel, M., Gunatilake, P., Podnar, G.: Robotic assistants for aircraft inspectors. IEEE Instrumentation & Measurement Magazine 1(1), 16–30 (1998)

    Article  Google Scholar 

  24. Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press (2006)

    Google Scholar 

  25. Xu, S., Weng, Y.: A new approach to estimate fractal dimensions of corrosion images. Pattern Recogn. Lett. 27(16), 1942–1947 (2006)

    Article  Google Scholar 

  26. Yamaguchi, T., Hashimoto, S.: Fast Crack Detection Method for Large-size Concrete Surface Images Using Percolation-based Image Processing. Mach. Vision Appl. 21(5), 797–809 (2010)

    Article  Google Scholar 

  27. Zaidan, B.B., Zaidan, A.A., Alanazi, H.O., Alnaqeib, R.: Towards corrosion detection system. Intl. Jour. Computer Science Issues 7(1), 33–35 (2010)

    Google Scholar 

  28. Zhang, X., Liang, R., Ding, Y., Chen, J., Duan, D., Zong, G.: The system of copper strips surface defects inspection based on intelligent fusion. In: Proc. IEEE Intl. Conf. Automation and Logistics, pp. 476–480 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Balearic Islands, Palma, Spain

    Alberto Ortiz, Francisco Bonnin-Pascual, Emilio Garcia-Fidalgo & Joan P. Company

Authors
  1. Alberto Ortiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francisco Bonnin-Pascual
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emilio Garcia-Fidalgo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joan P. Company
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Ortiz .

Editor information

Editors and Affiliations

  1. Info Syst Dept;Campus de Azurém, University of Minho, Guimarães, Portugal

    Luís Paulo Reis

  2. Electrical and Computer Engineering Dept, University of Porto, Porto, Portugal

    António Paulo Moreira

  3. Instituto Superior Tecnico - Torre Norte, University of Lisboa, Lisboa, Portugal

    Pedro U. Lima

  4. Engineering Dept., E. Ada Byron, Univ of Zaragoza, Computer & Systems, Zaragoza, Spain

    Luis Montano

  5. Automatic Control and Systems Eng. Dept., University of Malaga, Superior Technical, Málaga, Spain

    Victor Muñoz-Martinez

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Ortiz, A., Bonnin-Pascual, F., Garcia-Fidalgo, E., Company, J.P. (2016). Visual Inspection of Vessels by Means of a Micro-Aerial Vehicle: An Artificial Neural Network Approach for Corrosion Detection. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27149-1_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27148-4

  • Online ISBN: 978-3-319-27149-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation