Uncertainty Quantification to Assess the Generalisability of Automated Masonry Joint Segmentation Methods

Masonry-lined tunnels form a vital part of the world’s operational railway networks. However, in many cases their structural condition is deteriorating, so it is vital to undertake regular condition assessments to ensure their safety. In order to reduce costs and improve the repeatability of these assessments, automated deep learning-based tunnel analysis workflows have been proposed. However, for such methods to be applied in practice to a safety-critical situation, it is necessary to validate their conclusions. This study analysed how uncertainty quantification methods can be used to assess the test time performance of neural networks trained for masonry joint segmentation without the laborious labelling of additional ground truths. It applies test-time augmentation (TTA) and Monte Carlo dropout (MCD) to evaluate both the aleatoric and epistemic uncertainties of a selection of trained models. It then shows how these can be used to generate uncertainty maps to aid an engineer’s interpretation of the neural network output.