Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory

Authors:
Ahmad Osman;Valérie Kaftandjian;Ulf Hassler
Affiliations:
Fraunhofer Development Center X-ray Technologies, A Cooperative Department of IZFP Saarbrücken and IIS Erlangen, Dr-Mack-Str. 81, 90762 Fürth, Germany and National Institute of Applied S ...;National Institute of Applied Sciences INSA - Lyon, Non Destructive Testing using Ionising Radiations Laboratory CNDRI, Bat. St. Exupery, 25 Avenue Capelle, 69621 Villeurbanne, France;Fraunhofer Development Center X-ray Technologies, A Cooperative Department of IZFP Saarbrücken and IIS Erlangen, Dr-Mack-Str. 81, 90762 Fürth, Germany
Venue:
Pattern Recognition Letters
Year:
2011

Citing 2
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Uncertainty modelling using Dempster-Shafer theory for improving detection of weld defects

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Abstract

The aim of this work is to improve the classification of defects in X-ray inspection by developing a new method based on Dempster-Shafer data fusion theory where measured features on the detected objects are considered as information sources. From the histogram of features values on a learning database of manually classified objects, an automatic procedure is proposed to define a set of mass functions for each feature. The spatial repartition of features is divided into regions of confidence with corresponding mass functions. A smooth transition between regions is ensured by using fuzzy membership functions. The whole process is carried out without any expert intervention. Validation takes place on a testing database. Data fusion leads to a significant improvement of classification performances with respect to the actual system.