# TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation
## 4 Experiments and Results
### Experimental Results  
Results on TMCrack: We compare the proposed models with the models in Section 4.6 using MPA, recall, \(F_{1}\), MIou, dice score, and Jaccard on input images of size \(224\times 224\). All models were trained and tested on the TMCrack dataset, and the dataset settings were kept consistent. The results are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layers of CCT** & \(F_{1}\%\) & **MIou\%** & **Dice\%** \\ \hline
1 & 86.04 & 77.31 & 74.11 \\
2 & 85.80 & 77.05 & 73.71 \\
3 & 85.82 & 76.95 & 73.67 \\
4 & 85.79 & 76.88 & 73.59 \\
5 & 85.70 & 76.77 & 73.42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance with different numbers of layers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **P\%** & **R\%** & \(F_{1}\%\)** & **MIou\%** & **Dice\%** & **Jaccard\%** \\ \hline U-Net & 85.08 & 82.02 & 83.52 & 74.22 & 69.24 & 52.96 \\ PSPnet & 85.18 & 77.14 & 80.96 & 70.86 & 65.39 & 46.61 \\ DeepLab V3+ & 85.98 & 80.06 & 82.91 & 73.33 & 67.72 & 51.19 \\ UNet++ & 85.26 & 85.03 & 85.14 & 76.28 & 72.47 & 56.82 \\ Swin-Unet & 82.53 & 76.97 & 79.65 & 69.59 & 61.61 & 44.52 \\ UCTransNet & 82.79 & 83.48 & 83.13 & 73.87 & 68.93 & 52.69 \\ TMCrack-Net & 86.49 & 85.26 & 85.87 & 77.31 & 73.76 & 58.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performances of different methods on TMCrack.  
Table 5 shows that our network achieved an accuracy of 0.8649, a recall of 0.8526, an \(F_{1}\) value of 0.8587, an MIou of 0.7731, a dice value of 0.7376, and a Jaccard value of 0.5842 on the TMCrack test set. All six of these metrics are better than those of the other methods.  
Figure 8 shows the crack prediction results of different methods on the TMCrack test dataset. We tested the segmentation performance of various networks on simple and complex crack images. For cracks with simple or obvious precise shapes, most of them were detected in almost all tested models. For images with complex shapes and obscure cracks, TMCrack-Net had better performance. The PR curves of TMCrack-Net and other methods are shown in Figure 9a, which were used to evaluate the generalization level and implementation of each model from a qualitative point of view, and it can be seen from the figure that TMCrack-Net outperformed the other methods, segmenting more cracks and maintaining the accuracy of the locations.  
Results on Crack500: To verify the robustness of the model and the effectiveness of the improved method, the Crack500 dataset, widely used for crack detection, was selected. The improved model was retrained on Crack500 and compared with the model in Section 4.6. The results are shown in Table 6.  
Figure 9b shows that TMCrack-Net achieved the highest precision-recall curve, and had better generalization level and performance, as shown in Table 5. Its precision rate was 0.8809, recall rate was 0.8685, \(F_{1}\) value was 0.8747, MIou was 0.7944, and Dice value was 0.7641 on the Crack500 test dataset.  
Figure 8: Comparison of the results of different methods for six selected images from the test set.  
Figure 9: (**a**) Precision–recall curves for different models on TMCrack. (**b**) Precision–recall curves for different models on Crack500.  
Figure 10 illustrates the crack image prediction results from the test set. Figure 10 shows the images where the crack shapes vary from simple to complex to reflect the effect of crack shape on the detection of different models. It can be seen from the results that TMCRack-Ne has a better global grasp of the segmented cracks due to having more complete shapes compared to other segmentation networks.