# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments
### Effect of Different Settings of the Network Components
#### 4.5.2 Analysis of the Contextual Feature Enhancement Module  
The proposed contextual feature enhancement module includes two sub-modules: a pyramid poling module (PPM) and a squeeze-and-excitation block (SEB). In order to verify the effectiveness of the two sub-modules on model performance improvement, experiments are conducted. IoU and F1 score are used as the metrics and the results are shown in Table 9. The data in the table show that the introduction of the pyramid pooling module or the squeeze-and-excitation block alone can only improve the performance of the model to a small extent. The proposed contextual feature enhancement module can better improve the performance of the model with only a slight increase in FLOPs and number of model parameters, achieving the best results in both IoU and F1 scores.  
Our contextual feature enhancement module (CFEM) can be plug-and-played into any layer of the decoder network. To further explore the best optimization scheme, we  
Figure 12: Segmentation performance and efficiency of the networks with the CFEM in different decoder layers. (**a**) Comparison of the F1 score and FPS of the six networks; (**b**) FLOPs of the six networks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Structures** & **IoU (\%)** & **F1 Score (\%)** & **Parameters (10\({}^{6}\))** & **FLOPs (10\({}^{9}\))** \\ \hline PSC & 85.97 & 90.31 & 7.85 & 126.90 \\ DCSC & 87.68 & 92.36 & 9.16 & 314.13 \\ FSSC & 88.05 & 93.27 & 6.75 & 454.80 \\ MSSC & 87.33 & 92.16 & 5.82 & 101.58 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Segmentation performance and model complexity of the networks with different skip connection structures.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **PPM** & **SEB** & **Parameters (10\({}^{6}\))** & **FLOPs (10\({}^{9}\))** & **IoU (\%)** & **F1 Score (\%)** \\ \hline
1 & & & 5.95 & 102.15 & 88.62 & 92.42 \\
2 & & & 7.83 & 107.92 & 89.89 & 93.44 \\
3 & & & 5.99 & 102.16 & 89.24 & 93.05 \\
4 & & & 7.88 & 107.93 & 91.28 & 94.82 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation study of the contextual feature enhancement module.  
place CFEM at different layers of the decoder network while keeping the encoder network structure unchanged. F1 score, FPS, and FLOPs are used as the metrics, and the results are shown in Figure 12. It can be seen in the figure that as the number of network layers to which CFEM is applied increases, the network efficiency gradually decreases; however, the segmentation performance does not improve with it. This is because contextual features are semantic information, which is usually obtained by deep layers of the network. The shallow layers mainly explore structural features, and the multi-scale pooling and channel weight recalibration mechanism in CFEM will dilute the structural information in low-level feature maps, so that the network performance suffers. Taking both performance and efficiency into account, we apply the contextual feature enhancement module to the fourth and fifth layers of the decoder network.