Deep anomaly detection model for composite inspection in quadratic frequency modulated thermal wave imaging

Abstract

Thermographic deep learning models are object dependent and require huge training data to customize the model. On the other hand, the thermographic data is class imbalanced and scarce. This article proposes a novel unsupervised, object independent automatic deep anomaly detection model through an experimental investigation on two composite specimens using quadratic frequency modulated thermal wave imaging. The proposed methodology employs transfer learning to extract the deep features from thermal profiles by using only sound region features and surpassing the cross-material inspection limitation of supervised deep neural network models and provides better performance as quantified using F-Score and AUC. A depth map based visualization is further presented using chirp z-transform phase-based depth estimation of anomalies to facilitate an easier characterization.