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Intelligent Autonomous Systems 13 pp 1701–1713Cite as

A Knowledge-Based Approach to Crack Detection in Thermographic Images

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 302))

Abstract

This paper describes an innovative visual inspection system for the detection of small cracks in metal parts. Given the extremely low dimension of the defects to be detected, the system is based on a thermographic approach: defects are recognized analyzing the heat flux induced by an excitation. The system is able to analyze parts of very high complexity, like a crankshaft, thanks to the introduction of an articulated robot, used for moving the part. The system also benefits from a deep knowledge of the inspected part and of the imaging system: this is exploited to reduce the high number of artifacts and reflections that appear in thermographic images when heat sources are employed. The core of the inspection mechanism is a computer vision algorithm that is capable of analyzing the thermographic images, extract the thermal information, and exploit a Support Vector Machine (SVM) classifier to provide a final decision on the presence of a crack in the analyzed part.

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Notes

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    http://www.eigen.tuxfamily.org.

  2. 2.

    for an exhaustive introduction to the computer vision concepts here used, you can refer to “Computer Vision: principles and practice” [30].

  3. 3.

    http://www.opencascade.org/.

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Acknowledgments

The research leading to these results has received funding from the European Union, Seventh Framework Programme (FP7/2007-2013), under grant agreement No. 284607.

Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Padova, 35131, Padua, Italy

    Stefano Ghidoni, Mauro Antonello, Loris Nanni & Emanuele Menegatti

Authors
  1. Stefano Ghidoni
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  2. Mauro Antonello
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  3. Loris Nanni
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  4. Emanuele Menegatti
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Corresponding author

Correspondence to Stefano Ghidoni .

Editor information

Editors and Affiliations

  1. Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

    Nathan Michael

  3. Fachbereich Informatik, Technische Universität Kaiserslautern Arbeitsgruppe Robotersysteme, Kaiserslautern, Germany

    Karsten Berns

  4. Integrated Information Technology, Aoyama Gakuin University, Tokyo, Japan

    Hiroaki Yamaguchi

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Ghidoni, S., Antonello, M., Nanni, L., Menegatti, E. (2016). A Knowledge-Based Approach to Crack Detection in Thermographic Images. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_122

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  • DOI: https://doi.org/10.1007/978-3-319-08338-4_122

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08337-7

  • Online ISBN: 978-3-319-08338-4

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