Classifier Fusion for the Detection of Defects from Active Thermography

This paper presents a new method for detecting defects in composite materials examined by non-destructive testing using active thermography. The proposed method includes a fusion stage where the scores from multiple classifiers are fused under the mean-square error optimization criterion using alpha integration method. The goal is to improve the performance of individual classifiers based on different and sometimes complementary principles and that fusion can be used to exploit such a complementarity in both accuracy and variance. Several time-domain, frequency-domain, and statistics features were extracted from a dataset of thermography signals measured in composite material specimens. Seven individual classifiers were implemented. The results of fusion based on alpha integration were compared to the ones of the individual classifiers and the fusion by the mean showing the superiority of the proposed method in terms of several indices such as receiver operating characteristic and precision-recall curves.