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Image Fusion for Improved Detection of Near-Surface Defects in NDT-CE Using Unsupervised Clustering Methods

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Abstract

The capabilities of non-destructive testing (NDT) methods for defect detection in civil engineering are characterized by their different penetration depth, resolution and sensitivity to material properties. Therefore, in many cases multi-sensor NDT has to be performed, producing large data sets that require an efficient data evaluation framework. In this work an image fusion methodology is proposed based on unsupervised clustering methods. Their performance is evaluated on ground penetrating radar and infrared thermography data from laboratory concrete specimens with different simulated near-surface defects. It is shown that clustering could effectively partition the data for further feature level-based data fusion by improving the detectability of defects simulating delamination, voids and localized water. A comparison with supervised symbol level fusion shows that clustering-based fusion outperforms this, especially in situations with very limited knowledge about the material properties and depths of the defects. Additionally, clustering is successfully applied in a case study where a multi-sensor NDT data set was automatically collected by a self-navigating mobile robot system.

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Acknowledgments

The first author acknowledges the financial support of the Slovenian Research Agency through grant 1000-10-310156 and the Slovene Human Resources and Scholarship Fund through grant 11012-13/2012. The authors wish to thank Dr. Hans-Joachim Krause and colleagues for the radar-magnetic data set. Contributions and support from Assoc. Prof. Violeta Bokan Bosiljkov, Primož Murn and Dr. Parisa Shokouhi are acknowledged.

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Authors and Affiliations

  1. Institute of Mathematics, Physics and Mechanics, Jadranska 19, 1000 , Ljubljana, Slovenia

    Patricia Cotič & Zvonko Jagličić

  2. Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, 1000 , Ljubljana, Slovenia

    Patricia Cotič, Zvonko Jagličić & Vlatko Bosiljkov

  3. BAM Federal Institute for Materials Research and Testing, Division VIII.2, Unter den Eichen 87, 12205 , Berlin, Germany

    Patricia Cotič, Ernst Niederleithinger & Markus Stoppel

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  1. Patricia Cotič
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  2. Zvonko Jagličić
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Correspondence to Patricia Cotič.

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Cotič, P., Jagličić, Z., Niederleithinger, E. et al. Image Fusion for Improved Detection of Near-Surface Defects in NDT-CE Using Unsupervised Clustering Methods. J Nondestruct Eval 33, 384–397 (2014). https://doi.org/10.1007/s10921-014-0232-1

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