# TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation
## 5 Conclusions  
In this work, we compared the capability of different semantic segmentation models in mural crack segmentation. To improve the model's performance for mural crack segmentation, we proposed TMCrack-Net with feature fusion and a transformer. In addition, we built a mural crack library with 700 mural images annotated as a dataset. Our results showed that our model has some advantages for recognizing mural cracks. We use BiFPN to fuse the detailed information and semantic information of cracks, the CCT module to optimize the feature fusion to enhance the effective feature layer, and the FCA module to guide the fusion of encoded features and decoded features through an attention mechanism \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **P\%** & **R\%** & **F1\%** & **MIou\%** & **Dice\%** & **Jaccard\%** \\ \hline U-Net & 85.08 & 82.02 & 83.52 & 74.22 & 69.24 & 52.96 \\ U-Net+CCT & 87.03 & 83.55 & 85.25 & 76.37 & 72.44 & 56.79 \\ U-Net+BiFPN & 85.22 & 84.16 & 84.69 & 75.70 & 71.57 & 55.73 \\ U-Net+CCA & 86.58 & 84.80 & 85.68 & 76.97 & 73.38 & 57.95 \\ U-Net+CCT & 85.08 & 84.63 & 84.58 & 75.91 & 71.91 & 51.64 \\ U- & & & & & & \\ Net+CCT+FCA & 85.58 & 85.64 & 85.60 & 76.88 & 73.34 & 57.90 \\ TMCrack-Net & 86.49 & 85.26 & 85.87 & 77.31 & 73.76 & 58.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Ablation study on TMCrack for the each module. to reduce the ambiguity between them and segment more cracks. The main work and conclusions of this study are as follows:  
* We constructed a dataset of Tang Dynasty tomb chamber murals. The murals genuinely reflect the actual polo matches in the Tang Dynasty, providing informative physical data for the study of polo in the Tang Dynasty, which is of great historical value. The mural background is complex, having people, horses, and ancient mountains and rocks. Complex backgrounds are suitable for reflecting the model's applicability in complex backgrounds. * Different layers of convolutional layers have different image features. In the shallow network, the features contain detailed information about the cracks, and in the higher layers, the features are represented as the semantic features of the cracks. The fusion pre-screening of information at different scales facilitates the extraction of crack information. * In order to obtain the number of CCT modules and self-attentive heads, the experimental comparison results in Section 4.8 show that the results are better when the number of transformer modules is one and the number of self-attentive heads is six. P, F1, MIou, dice score, and Jaccard improved by 0.0181, 0.0056, 0.0067, 0.0087, and 0.0106 compared to the model using only convolution. * The FCA module is designed to integrate the encoding features with the decoding features. In Section 4.8, the results showed that by adding the FCA module, the MIou value of the model was improved by 0.0275, the \(p\) value by 0.015, recall by 0.0278, \(F_{1}\)-score by 0.0216, dice score by 0.0414, and Jaccard by 0.0499. * The proposed model TMCrack-Net was compared with semantic segmentation networks of different structures. The evaluation metrics and visualization results in Section 4.7 show that TMCrack-Net achieved the highest values of P, R, F1, MIou, dice score, and Jaccard with 0.8649, 0.8526, 0.8587, 0.7731, 0.7376, and 0.5842, respectively. In the future, we will further address the limitations of our model. We will improve the model's accuracy and enhance the model for segmenting cracks in more complex mural images. We will investigate better fusion methods with attention mechanisms for improvement. As the number of tomb murals is scarce and precious, we will continue to collect more murals images to build a larger dataset so that the model can better learn the crack features. Conceptualization and methodology, M.W. and M.J.; validation, M.J.; writing--original draft preparation, M.J.; writing--review and editing, M.J. and M.W. ; supervision, M.W. ; funding acquisition and resources, M.W. ; visualization, M.J.; data formatting, J.W.