Lightweight High-Precision Monitoring of Damage Locations in Dunhuang Murals Based on Improved YOLACT

Abstract As an important cultural heritage of China and a world treasure, the Dunhuang murals have undergone thousands of years of weathering, oxidation, and human interference, resulting in extensive damage such as cracks, peeling, and fading on their surfaces. Additionally, due to their complex colour palette, it is challenging to quickly identify damaged areas, leading to a situation where the rate of damage to the Dunhuang murals far exceeds the rate of restoration. To achieve efficient and precise monitoring of damage to Dunhuang murals and support cultural heritage preservation efforts, this paper proposes an improved lightweight, high-precision method for monitoring the location of damage to Dunhuang murals based on YOLACT. First, addressing the limitation of traditional YOLACT models in extracting fine-grained texture features, a residual self-attention mechanism capable of extracting global features is introduced to enhance the model's feature extraction capability (mAP50: 79.2064 $%$). Subsequently, by introducing a depth-separable convolutional architecture, the goal of lightweight design is achieved while maintaining high accuracy (parameter count: 715.12 k, floating-point operations: 70.219 M). Finally, to validate the model's effectiveness, this paper conducted validation on a public dataset. Experimental results show that, compared to three mainstream models, the proposed method achieves the highest mAP50 and the smallest parameter count and floating-point operations.