An automatic detection method of the mural shedding disease using YOLOv4

Chunmei Hu; Yuxin Dong; Guofang Xia; Xi Liu

doi:10.1117/12.2625707

9 December 2021 An automatic detection method of the mural shedding disease using YOLOv4

Chunmei Hu, Yuxin Dong, Guofang Xia, Xi Liu

Proceedings Volume 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021); 121290O (2021) https://doi.org/10.1117/12.2625707
Event: 2021 International Conference on Environmental Remote Sensing and Big Data, 2021, Wuhan, China

Abstract

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.

Citation Download Citation

Chunmei Hu, Yuxin Dong, Guofang Xia, and Xi Liu "An automatic detection method of the mural shedding disease using YOLOv4", Proc. SPIE 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021), 121290O (9 December 2021); https://doi.org/10.1117/12.2625707

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