# SCCDNet: A Pixel-Level Crack Segmentation Network
## 3 Results and Experimental Evaluations
### Ablation Study and Discussion
#### 3.5.1 Ablation Analysis of the SSE Module  
In Section 2.2, we specifically introduced the structure and function of the SSE module. In order to prove its contribution to the model, we compare the effects of different connection strategies on the accuracy of crack segmentation. In order to prove the generalization of the model, we also use 5-fold cross-validation to prove the influence of the SSE module on the performances. The results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **m-Train\_loss** & **m-Valid\_loss** & **m-Precision** & **m-Recall** & **m-F-Score** & **m-Dice** & **m-IoU** \\ \hline U-Net & 0.0245 & 0.0284 & 0.6901 & 0.8069 & 0.7439 & 0.7160 & 0.5967 \\ SegNet & 0.0275 & 0.0404 & 0.6349 & 0.7592 & 0.6908 & 0.6226 & 0.5061 \\ SCCD-D16 & 0.0248 & 0.0363 & 0.7161 & 0.8246 & 0.7103 & 0.6462 & 0.5320 \\
**SCCD-D32** & 0.0209 & **0.0270** & **0.7199** & 0.8300 & 0.7710 & **0.7457** & **0.6316** \\ SCCD-D64 & **0.0203** & **0.0270** & 0.7192 & **0.8320** & **0.7714** & 0.7441 & 0.6308 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of 5-fold cross-validation. The best average scores are in bold.  
As shown in Table 3, None-SSE represents the model in Figure 1 without any SSE module, that is, completely eliminates the contribution of the SSE module; SSE1 represents the model that only remains the SSE1 module which connects two feature maps with a resolution of 448\(\times\)448, while SSE2, SSE3, SSE4, SSE5, respectively, indicate that only the corresponding SSE modules in Figure 1 remained, and the influence of other SSE modules on the results is excluded. Fully-SSE means that all 5 SSE modules remained, and the network structure is shown in Figure 1. In order to prove the generalization of the model, we also use 5-fold cross-validation to prove the influence of the SSE module on the experimental results.  
As shown in Table 3, compared to None-SSE, SSE1, SSE2, and SSE3 do not greatly improve the model performance. The reason is that the feature maps connected by the three modules are too far apart, so the difference between the features is too large. Although the value of the shallow feature map is recalibrated through the attention gate, it is difficult to find similar features in the subsequent learning after concatenating, so the improvement of model performance is limited; SSE4 and SSE5 improve m-Precision more greatly and improve m-Recall less. Therefore, when using the SSE module to connect two feature maps that are close, the model's ability to resist error detection is enhanced significantly. In addition, compared with the None-SSE model, all models with the SSE module have improved m-F-score, m-Dice, and m-IoU, which enhances the model's crack segmentation capabilities. The Fully-SSE model enhances the model's ability to resist error detection and missed detection and has obtained the greatest evaluation performances except for m-Precision. We verified that all SSE modules can improve the performance of the model. Although some improvements are not obvious, considering that the SSE module basically does not increase the amount of model parameters, we use Fully-SSE as the best model.