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 .

翻译：计算机视觉在建筑物病害检测领域已引起研究者长期关注。基于视觉的裂缝检测作为一种非破坏性评估技术，尤其适用于文化遗产建筑领域——该领域受到严格法规约束，即使是简单的干预措施也不被允许。近年来，基于各类图像的浅层与深层机器学习架构逐渐占据优势地位。本文提出了一种针对石砌体墙面的裂缝检测方法。该方法将裂缝检测视为RGB（红绿蓝）图像块上的无监督异常检测问题。为此，我们部署并改进了当前最先进的卷积神经网络架构，使其能够将测试图像或图像块二分类为"裂缝"或"无裂缝"，并高精度定位RGB图像中的裂缝。模型在多个测试场地、互联网随机图像及作者自采图像上进行了评估。结果表明，特定网络在训练轮次需求较小的情况下仍展现出卓越性能，其准确率可与需要大量训练数据的复杂高计算量方法相媲美。源代码发布于GitHub（https://github.com/pagraf/Crack-detection），数据集发布于Zenodo（https://doi.org/10.5281/zenodo.6516913）。