  Vacuum Insulated Glazing (VIG) is a highly thermally insulating window
technology, which boasts an extremely thin profile and lower weight as compared
to gas-filled insulated glazing units of equivalent performance. The VIG is a
double-pane configuration with a submillimeter vacuum gap between the panes and
therefore under constant atmospheric pressure over their service life. Small
pillars are positioned between the panes to maintain the gap, which can damage
the glass reducing the lifetime of the VIG unit. To efficiently assess any
surface damage on the glass, an automated damage detection system is highly
desirable. For the purpose of classifying the damage, we have developed,
trained, and tested a deep learning computer vision system using convolutional
neural networks. The classification model flawlessly classified the test
dataset with an area under the curve (AUC) for the receiver operating
characteristic (ROC) of 100%. We have automatically cropped the images down to
their relevant information by using Faster-RCNN to locate the position of the
pillars. We employ the state-of-the-art methods Grad-CAM and Score-CAM of
explainable Artificial Intelligence (XAI) to provide an understanding of the
internal mechanisms and were able to show that our classifier outperforms
ResNet50V2 for identification of crack locations and geometry. The proposed
methods can therefore be used to detect systematic defects even without large
amounts of training data. Further analyses of our model's predictive
capabilities demonstrates its superiority over state-of-the-art models
(ResNet50V2, ResNet101V2 and ResNet152V2) in terms of convergence speed,
accuracy, precision at 100% recall and AUC for ROC.
