The cultural heritage buildings (CHB), which are part of mankind's history and identity, are in constant danger of damage or in extreme situations total destruction. That being said, it's of utmost importance to preserve them by identifying the existent, or presumptive, defects using novel methods so that renovation processes can be done in a timely manner and with higher accuracy. The main goal of this research is to use new deep learning (DL) methods in the process of preserving CHBs (situated in Iran); a goal that has been neglected especially in developing countries such as Iran, as these countries still preserve their CHBs using manual, and even archaic, methods that need direct human supervision. Having proven their effectiveness and performance when it comes to processing images, the convolutional neural networks (CNN) are a staple in computer vision (CV) literacy and this paper is not exempt. When lacking enough CHB images, training a CNN from scratch would be very difficult and prone to overfitting; that's why we opted to use a technique called transfer learning (TL) in which we used pre-trained ResNet, MobileNet, and Inception networks, for classification. Even more, the Grad-CAM was utilized to localize the defects to some extent. The final results were very favorable based on those of similar research. The final proposed model can pave the way for moving from manual to unmanned CHB conservation, hence an increase in accuracy and a decrease in human-induced errors.

翻译：文化历史建筑作为人类历史与身份的重要组成部分，始终面临受损乃至彻底毁灭的持续威胁。因此，采用创新方法识别其现存或潜在缺陷以实现及时且高精度的修复工作至关重要。本研究旨在利用新型深度学习方法保护伊朗境内的文化历史建筑——这一目标在伊朗等发展中国家长期被忽视，这些国家仍依赖人工甚至古老方法进行文化遗产保护，需要直接的现场监督。卷积神经网络在图像处理领域已证明其有效性与卓越性能，是计算机视觉领域的基石技术，本文亦不例外。由于缺乏足够的文化历史建筑图像样本，从零训练CNN极易导致过拟合；为此我们采用迁移学习技术，使用预训练的ResNet、MobileNet和Inception网络进行分类。更进一步，我们运用Grad-CAM实现了缺陷位置的有限定位。基于同类研究的对比验证，最终结果极为理想。所提出的最终模型可为从人工向无人化文化历史建筑保护的转型铺平道路，从而实现精度提升与人为误差的显著降低。