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Sensors 2016, 16(1), 105; doi:10.3390/s16010105

Decision-Level Fusion of Spatially Scattered Multi-Modal Data for Nondestructive Inspection of Surface Defects

1
Division 8.5 Micro NDE, Federal Institute for Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany
2
Department of Civil and Environmental Engineering, The Pennsylvania State University, 215 Sackett Bldg., University Park, PA 16802, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Piervincenzo Rizzo
Received: 5 November 2015 / Revised: 7 January 2016 / Accepted: 12 January 2016 / Published: 15 January 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1907 KB, uploaded 18 January 2016]   |  

Abstract

This article focuses on the fusion of flaw indications from multi-sensor nondestructive materials testing. Because each testing method makes use of a different physical principle, a multi-method approach has the potential of effectively differentiating actual defect indications from the many false alarms, thus enhancing detection reliability. In this study, we propose a new technique for aggregating scattered two- or three-dimensional sensory data. Using a density-based approach, the proposed method explicitly addresses localization uncertainties such as registration errors. This feature marks one of the major of advantages of this approach over pixel-based image fusion techniques. We provide guidelines on how to set all the key parameters and demonstrate the technique’s robustness. Finally, we apply our fusion approach to experimental data and demonstrate its capability to locate small defects by substantially reducing false alarms under conditions where no single-sensor method is adequate. View Full-Text
Keywords: multi-sensor data fusion; density estimation; scattered data; defect detection; nondestructive testing; registration errors multi-sensor data fusion; density estimation; scattered data; defect detection; nondestructive testing; registration errors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Heideklang, R.; Shokouhi, P. Decision-Level Fusion of Spatially Scattered Multi-Modal Data for Nondestructive Inspection of Surface Defects. Sensors 2016, 16, 105.

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