Vision-based periodic examination of the deterioration of stone monuments over time is labour and time intensive. Especially, in cases involving large-scale immovable cultural heritage, the workforce is considerably increased, along with the possibility of occurrence of errors. Any misdiagnoses in the deterioration may cause irreversible structural problems in monuments, and thus, it is necessary to develop alternative examination methods. Computer-vision methods represent an effective solution to eliminate both human errors and difficulties in the field. Therefore, this study aims to adopt the Mask R–CNN algorithm, which is a computer-vision method, to detect and map the deteriorations observed in the Gümüşler archaeological site and monastery (cracks, discontinuities, contour scaling, missing parts, biological colonization, presence of higher plants, deposits, efflorescence, and loss of fresco). First, 1740 images were collected from the site, and the model was trained by labelling the distortions in these images according to their types. Later, the model was tested on four outdoor and two indoor views. The developed model achieved an average precision ranging between 91.591% and 100%, and the mean average precision was 98.186%. These results demonstrated that the proposed algorithm can enable mapping to promptly and automatically detect the deterioration in large monuments.

基于视觉的定期检查石碑随着时间的推移而变质是一项费时费力的工作。特别是在涉及大规模的不动产文化遗产的情况下，劳动力会大大增加，并且可能发生错误。劣化的任何误诊都可能会导致纪念碑中出现不可逆的结构问题，因此，有必要开发其他检查方法。计算机视觉方法代表了一种有效的解决方案，可以消除人为错误和现场困难。因此，本研究旨在采用Mask R–CNN算法（这是一种计算机视觉方法），以检测并绘制在Gümüşler考古遗址和修道院中观察到的恶化情况（裂缝，不连续性，轮廓缩放，缺失的部分，生物殖民地，高等植物的存在，沉积物，风化和壁画的丢失）。首先，从现场收集了1740张图像，并通过根据图像的类型标记这些图像的畸变来训练模型。后来，该模型在四个室外和两个室内视图上进行了测试。开发的模型实现了91.591％到100％的平均精度，平均平均精度为98.186％。这些结果表明，所提出的算法能够使映射迅速并自动检测大型纪念物的退化。开发的模型实现了91.591％到100％的平均精度，平均平均精度为98.186％。这些结果表明，所提出的算法能够使映射迅速并自动检测大型纪念物的退化。开发的模型实现了91.591％到100％的平均精度，平均平均精度为98.186％。这些结果表明，所提出的算法能够使映射迅速并自动检测大型纪念物的退化。