Ancient architectures have a lot of distinctive patterns on their component surfaces. But due to the wind and the Sun, the patterns will present extensive deficiencies, which are not conducive to the routine inspections and the subsequent repair work. To overcome these limits, an automatic deep learning-based method of segmentation, inpainting, and classification for defective patterns on ancient architecture is proposed. First, You Only Look At CoefficienTs, which is a real-time instance segmentation network, is applied to obtain the mask of the defective parts. Then Generative Image Inpainting with Contextual Attention, which is an image inpainting algorithm, is used to reconstruct the defective parts. Finally, Residual Neural Network-50 is used to classify the reconstructed images. In this paper, three types of dragon motifs in the Forbidden City were studied. The results show that the classification accuracy of the reconstructed images is increased by an average of 13.1%, with the maximum increasing by 23.5%. The proposed methods can prepare for future routine inspections in advance.

中文翻译：

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使用多种深度学习算法自动分割、修复和分类古建筑上的缺陷模式

古代建筑在其构件表面上有许多独特的图案。但由于风吹日晒，花纹会出现大面积缺陷，不利于日常检查和后续维修工作。为了克服这些限制，提出了一种基于自动深度学习的古建筑缺陷模式分割、修复和分类方法。首先，You Only Look At CoefficienTs 是一个实时实例分割网络，用于获取缺陷部分的掩码。然后使用具有上下文注意的生成图像修复，这是一种图像修复算法，用于重建缺陷部分。最后，Residual Neural Network-50 用于对重建图像进行分类。在本文中，研究了紫禁城的三种龙纹。结果表明，重建图像的分类精度平均提高了13.1%，最大提高了23.5%。提出的方法可以提前为未来的例行检查做好准备。