# A Multi-Scale Contextual Information Enhancement Network for Crack Segmentation
## 4 Experiments
### Ablation Experiments  
To verify the effectiveness of improved schemes, such as multi-scale skip connection (MSSC), the contextual feature enhancement module (CFEM), and deformable convolution (DConv), an ablation study is carried out on the DeepCrack-DB dataset. IoU and F1 score metrics are used as the metrics and the results are shown in Table 5. From experiment 1 and experiment 2, we can learn that the IoU is improved by 1.36% and the F1 score is improved by 1.85% after using the multi-scale skip connection structure. This indicates that our multi-scale skip connection structure can help the network to better capture fine-grained spatial information, such as crack edges. Comparing experiments 2 and 3, it can be seen that the IoU and the F1 score improve by 2.48% and 1.58%, respectively, which indicates that the network can improve its ability to adapt to complex backgrounds and distinguish between interference information and segmented objects. Comparing experiment 3 and experiment 4, the introduction of deformable convolution improves the IoU by 1.47% and the F1 score by 1.08%, which shows that the use of deformable convolution is also an effective method to improve the segmentation performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Parameters (10\({}^{6}\))** & **FLOPs (10\({}^{9}\))** & **FPS (Frames/s)** \\ \hline DeepCrack & 14.72 & 181.11 & 26.28 \\ DeepLab v3+ & 16,385 & 187.69 & 12.4 \\ U-Net & 7.85 & 126.9 & 54.32 \\ UNet++ & 9.16 & 314.13 & 30.48 \\ Unet 3+ & 6.75 & 454.8 & 28.16 \\ MCIE-Net & 7.87 & 107.93 & 24.36 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Complexity and efficiency comparison of different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **IoU (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline DeepCrack & 78.77 & 83.76 & 90.86 & 87.37 \\ DeepLab v3+ & 76.03 & 96 & 77.22 & 84.91 \\ U-Net & 85.59 & 98.22 & 86.08 & 91.31 \\ UNet++ & 86.31 & 98.43 & 86.67 & 91.77 \\ Unet 3+ & 89.61 & 98.32 & 90.1 & 93.75 \\ MCIE-Net & 90.45 & 98.46 & 92.43 & 94.22 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different methods on the CCSD dataset.  
To highlight the advantages of our multi-scale skip connection in extracting spatial feature information and obtaining refined crack segmentation results, we select some images with tiny cracks from the test sets of DeepCrack-DB, CFD, and CCSD datasets to form the mixed test set A (MTSA), and evaluate the segmentation results of the images. The MTSA has a total of 51 images, of which 20 are selected from DeepCrack-DB, 7 are selected from CFD, and 24 are selected from CCSD. Our network is compared to Network A, whose structure is mostly the same as MCIE-Net, but without the multi-scale skip connection. Some representative samples are shown in Figure 10, and Table 6 shows the quantitative comparison of the IoU, precision, recall, and F1 score. As can be seen in Figure 10, Network A fails to extract refined spatial information and recognize the tiny cracks. Meanwhile, as shown in Table 6, our MCIE-Net achieves the best results for IoU, recall, and F1 score metrics, and the result for the precision metric is very close to that of Network A. It can be concluded that our multi-scale skip connection plays an important role in obtaining refined crack segmentation results.  
segmentation results of images in MTSB and our MCIE-Net is compared to Network B, whose structure is mostly the same as the propose network, but without the contextual feature enhancement module. Table 7 shows a quantitative comparison of the IoU, precision, recall, and F1 score metrics, in which our MCIE-Net achieves the best results in all four metrics. The segmentation results in Figure 11 highlight the advantage of our MCIE-Net in distinguishing between cracks and noise interference in the background, which is attributed to the contextual feature enhancement module.