Explainable AI for Reliable Defect Recognition and Localization in Non-Destructive Testing With Millimeter-Wave Imaging

Millimeter-wave imaging is a promising technique for non-destructive testing (NDT). It is a contactless sensing modality, it can penetrate many optically opaque materials, and millimeter-waves are not harmful to the operator. To enable automated defect recognition in millimeter-wave images, artificial intelligence (AI), such as deep learning, has been investigated in the research community. One problem associated with deep learning, however, is that the network’s decisions are not based on analytical criteria, but on a non-linear optimization process during training. Because of this, the bases for the network’s predictions are not transparent. This “black box” behavior limits confidence, which restricts the use of deep learning in NDT so far. To make deep learning networks more transparent, explainable AI (XAI) techniques have been introduced in the machine learning community. In this study, we introduce explainable AI to millimeter-wave based defect detection. It enables an understanding of how the deep-learning network classifies defects. At the same time, shortcomings of the deep learning network can be made visible. Since explainable AI provides visual maps of important image regions, it is possible to perform a basic defect localization without the need for a specific localization network. This approach is termed “weakly supervised object localization”. In this paper we discuss both defect classification and defect localization based on XAI techniques. Experimental demonstration is performed by means of a millimeter-wave imaging dataset of PVC objects with artificial defects.