A Knowledge-Based Approach to Crack Detection in Thermographic Images

  • Stefano Ghidoni
  • Mauro Antonello
  • Loris Nanni
  • Emanuele Menegatti
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 302)


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.


Computer vision Automated visual inspection Thermographic image analysis Pattern recognition 



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


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Stefano Ghidoni
    • 1
  • Mauro Antonello
    • 1
  • Loris Nanni
    • 1
  • Emanuele Menegatti
    • 1
  1. 1.Department of Information EngineeringUniversity of PadovaPaduaItaly

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