基于轻量化多尺度融合注意力网络的古代壁画脱落区域自动标定

Integration of Multi-scale and Attention Mechanism for Ancient Mural Detachment Area Localization

针对古代壁画脱落区域难以准确自动标定的问题,文中提出了一种基于多尺度融合注意力网络的轻量化网络模型。首先,提出多尺度融合注意力模块使网络能够学习到更多不同尺度的特征,并重点关注其中更重要的特征,从而提高标定壁画脱落区域的准确率。在提出的多尺度融合注意力模块中采用了深度可分离卷积,使网络模型更加轻量化。其次,采用交叉熵损失与Dice得分相结合的方式作为损失函数,并采用Adam优化器进一步提高标定壁画脱落区域的准确率。此外,构建了敦煌莫高窟壁画和云南石屏罗色庙壁画数据集,并对其脱落区域进行了人工标定。实验结果表明,所提网络模型能够准确地标定出古代壁画中的脱落病害区域。与现有深度学习方法进行对比,所提模型的参数量显著减少,且在主观视觉质量、客观评价指标以及泛化性能上都有更好的表现。

In response to the challenging problem of accurately automating the localization of peeling areas in ancient murals,this paper proposes a lightweight network model based on a multi-scale fusion attention network.Firstly,a multi-scale fusion attention module is introduced to enable the network to learn features at different scales,with a focus on the most critical features,thus improving the accuracy of mural missing area localization.Deep separable convolutions are employed in the proposed multi-scale fusion attention module to make the network model more lightweight.Secondly,a combination of cross-entropy loss and Dice score is used as the loss function,and the Adam optimizer is applied to further enhance the accuracy of mural missing area localization.Additionally,datasets of Dunhuang Mogao Grottoes murals and Yunnan Shiping Luose Temple murals are constructed,and their peeling areas are manually annotated.Experimental results demonstrate that the proposed network model accurately localizes peeling regions in ancient murals.In comparison with existing deep learning methods,this model significantly reduces the number of parameters and exhibits better performance in terms of subjective visual quality,objective evaluation metrics,and generalization capabilities.