Learning multi-path CNN for mural deterioration detection

Abstract

Mural deterioration easily destroys valuable paintings and must be monitored frequently for preventive protection. Deterioration detection in mural images is often manually labeled and is a preprocessing step for mural protection and restoration. Many deterioration forms are commonly invisible with only one lighting condition because mural deterioration is caused by changes in the material and plaster layer. This study addresses mural deterioration detection through a multi-path convolutional neural network (CNN), which takes images of a scene with multiple lightings as inputs and generates a binary map that indicates deterioration regions. We design an eight-path CNN in which seven paths are utilized for basic feature extraction from lighted images, and the remaining path is responsible for cross feature fusion. This mechanism enables our method to not only identify suitable features for different lightings but also utilize these features collaboratively through cross feature fusion. Furthermore, we build two realistic mural deterioration datasets of real-world mural deterioration and briquettes that simulate the cave deterioration. Extensive experiments verify the effectiveness and efficiency of our method.