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Image fusion for improved detection of near-surface defects in NDT-CE using unsupervised clustering methods

  • 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 materialThe 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.zeige mehrzeige weniger

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Metadaten
Autor*innen:Patricia Cotic, Z. Jaglicic, Ernst NiederleithingerORCiD, Markus Stoppel, V. Bosiljkov
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Journal of nondestructive evaluation
Jahr der Erstveröffentlichung:2014
Verlag:Plenum Press
Verlagsort:New York, NY
Jahrgang/Band:33
Ausgabe/Heft:3
Erste Seite:384
Letzte Seite:397
Freie Schlagwörter:Cluster analysis; Concrete; Data fusion; Defect detection; Defects; Image fusion; Non-destructive testing; Radar; Thermography; Ultrasonics
DOI:10.1007/s10921-014-0232-1
ISSN:0195-9298
ISSN:1573-4862
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:20.02.2016
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:27.08.2015
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