Application of deep learning algorithms for identifying deterioration in the ushnisha (Head Bun) of the Leshan Giant Buddha

Abstract

Abstract The Leshan Giant Buddha’s ushnisha (Head Bun) has suffered from loss of lime plaster, cracks, and biological damage, compromising its structural integrity and reducing the effectiveness of the drainage system in the Buddha's head. The infiltration of moisture has led to water damage within the statue, significantly accelerating its weathering. This situation urgently requires protection and reinforcement measures. Detecting deterioration in the ushnisha is a crucial step in the preservation process. In this study, we utilized two deep learning models for pixel-level semantic segmentation of the damage. Due to the small size of the cracks, a weighted loss function was applied to improve both the training speed of the model and the efficiency of crack identification. This weighting strategy proved effective for both models. The weighted K-Net model achieved a mean accuracy (mAcc) of 90.23% and a mean intersection-over-union (mIoU) of 69.55%, with a damage segmentation speed of 7 images per second, which is 1309 times faster than manual segmentation. By applying the trained deep learning models to re-examine the ushnisha, we successfully identified damage that had been overlooked during manual annotation. Using the model’s enhanced results, we conducted a comprehensive quantification of the damage across all ushnisha and identified the most severely affected areas. Additionally, we performed a model interpretability analysis to explain the decision-making process and principles of the deep learning models. This research provides significant practical value for detecting and quantifying damage in the Leshan Giant Buddha.

Keywords

• Leshan Giant Buddha
• Ushnisha
• Damage segmentation
• Deep learning
• Weighted loss function
• Model interpretability