  Computer vision for detecting building pathologies has interested researchers
for quite some time. Vision-based crack detection is a non-destructive
assessment technique, which can be useful especially for Cultural Heritage (CH)
where strict regulations apply and, even simple, interventions are not
permitted. Recently, shallow and deep machine learning architectures applied on
various types of imagery are gaining ground. In this article a crack detection
methodology for stone masonry walls is presented. In the proposed approach,
crack detection is approached as an unsupervised anomaly detection problem on
RGB (Red Green Blue) image patches. Towards this direction, some of the most
popular state of the art CNN (Convolutional Neural Network) architectures are
deployed and modified to binary classify the images or image patches by
predicting a specific class for the tested imagery; 'Crack' or 'No crack', and
detect and localize those cracks on the RGB imagery with high accuracy. Testing
of the model was performed on various test sites and random images retrieved
from the internet and collected by the authors and results suggested the high
performance of specific networks compared to the rest, considering also the
small numbers of epochs required for training. Those results met the accuracy
delivered by more complex and computationally heavy approaches, requiring a
large amount of data for training. Source code is available on GitHub
https://github.com/pagraf/Crack-detection while datasets are available on
Zenodo https://doi.org/10.5281/zenodo.6516913 .

Computer Vision and Pattern Recognition