Intelligent labeling of areas of wall painting with paint loss disease based on multi-scale detail injection U-Net

In the preservation and restoration of murals, labeling and recording the location and size of the paint loss disease can bring convenience to the subsequent restoration work. At present, the most common method of disease labeling is to draw the disease area manually on an orthophoto map by human-computer interaction. However, this method not only requires much time, but also leads to different labeling results due to different experts' experience. In recent years, with the development of artificial intelligence, machine learning and other technologies, it is possible to realize intelligent labeling through image processing and other methods. Therefore, this paper focuses on the mural paint loss disease and tries to explore the intelligent disease labeling method, hoping to efficiently and accurately mark the paint loss disease. In this paper, firstly, the disease labeling is transformed into an image segmentation problem, and proposes a mural paint loss disease labeling based on U-Net. However, it was experimentally found that much detailed information is often lost when the U-Net is used directly. Therefore, this paper further proposes multi-scale detail injection U-Net, including the constructed multi-scale module and the method of injecting shallow features into in-depth features, which could effectively extract more abundant edge information and improve the labeling accuracy. Furthermore, we demonstrate that the method proposed in this paper could actually achieve the intelligent labeling of the paint loss disease through the murals of the Liao Dynasty Feng Guo Temple in Yi County, Jinzhou City, China.