Improvement of X-ray castings inspection reliability by using Dempster-Shafer data fusion theory
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 (C) 2010 Elsevier B V All rights reserved
castings inspection data fusion x-ray imaging features extraction confidence levels dempster-shafer theory