Abstract: To address the issues of low detection precision and poor real-time performance in the process of grotto mural detachment and damage detection, we propose a grotto mural damage detection method based on a lightweight neural network and multiple attention mechanisms. First, Ghost Conv is introduced to complete lightweight feature extraction and reduce model complexity. Second, we add a double attention mechanism to increase the tendency of feature extraction and accelerate model convergence. Finally, we use a weighted bidirectional feature pyramid network to efficiently fuse feature information and complete prediction by composite scaling. The experimental results show that the improved algorithm reduces the number of network layers by 34.40%. The number of parameters and floating point operations are reduced by 62.98% and 68.77%, respectively, and the model volume is compressed by 62.78%. The detection precision is 64.7%, and the real-time detection speed is improved from 63.60 frame/s to 97.56 frame/s, which is approximately 53.39%.