An automatic detection method of the mural shedding disease using YOLOv4

Murals are an important part of China's cultural heritage that have high historical, scientific and cultural values. The traditional methods of extracting the diseases of murals are mainly artificial measurement and orthographic drawing, which are inefficient for the rapid statistics of large-scale mural diseases. To solve the above problems, a disease data set was established based on mural orthophoto images. And the image deep learning YOLOv4 algorithm was used to train the data set. Through comparative experiments, the most suitable method for YOLOv4 network detection was found to make the data set, so as to realize automatic rapid recognition of mural orthophoto images and express the disease information. Through experiments, it is proved that the accuracy of disease identification by this research method reaches 86.51%, and the extraction results can provide favorable data support for scientific and technological protection of cultural relics.