## 4 Training Result and Comparison
### Comparative Study  
To test the performance of SoUNet, we selected four methods based on deep learning for comparative study: (1) SegNet [24] is a fully convolutional network, which was used for semantic segmentation. It has also been proposed for crack identification of  
Figure 11: Fusion process and result of segmented image and edge image: (**a**) Linear fusion process of output images for (**b**) single crack, (**c**) multiple cracks, (**d**) net-shaped crack. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Metrics** & & & \\ \hline  & MIoU & MPA & ODS-F & OIS-F \\ SoUNet-Output-1 & 67.17 & 72.31 & — & — \\ SoUNet-Fusion-12 & **69.64** & 78.25 & 31.52 & 32.99 \\ SoUNet-Fusion-13 & 69.32 & 80.33 & **33.14** & **34.11** \\ SoUNet-Fusion-14 & 68.29 & 81.54 & 32.08 & 33.15 \\ SoUNet-Fusion-15 & 65.92 & **81.89** & 29.46 & 30.63 \\ SoUNet-Fusion-16 & 60.39 & 81.51 & 25.66 & 26.73 \\ SoUNet-Fusion-17 & 69.42 & 80.14 & 33.08 & 34.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of the model. Figure 12: Statistics of the crack’s width at different positions: (**a**) the width of the initial position, middle position, and end position of the crack; (**b**) the width error statistical charts of SoUNet and U-NET. concrete pavement, asphalt pavement, and bridge deck [44]; (2) HED [38], which is an edge detection model with high performance that can also be used for crack detection; (3) VGG16-U-Net [45]: U-Net is a high-performance semantic segmentation network [25]. Its improved structure, VGG16 U-Net, has been used to detect surface defects in concrete and asphalt [46]. Comparing the recognition performance of the following strategies for the proposed SoUNet model is necessary: (1) SoUNet-Basic: The basic side-output U-Net structure, the side-output part plays the role of deep supervision and improves the model learning efficiency; (2) SoUNet-BN: Adding a batch normalization layer based on SoUNet-Basic. The BN layer can accelerate the training process; (3) SoUNet-GF: A and B are taken as the original image and guide image, respectively, from the outputs of SoUNet-BN and then perform the guided filter operation; (4) SoUNet-Fusion: This is the same as SoUNet-Fusion-13 in Table 3 of Section 4.2. Deep-learning-based methods can be applied to image recognition tasks, but these methods are only suitable for specific scenes and tasks in most cases. Poor generalization performance is one of the main drawbacks of these methods. To further test the generalization performance of SoUNet in the crack detection task, the FISSURES dataset [50] was downloaded. This dataset is similar to our dataset. The preprocessing method in Section 3.1 is used to process the dataset and make those sizes suitable for the network model. Finally, they were sent to the trained model to view the results. Table 4 shows the evaluation results of the seven methods on the two datasets. Our test set is divided in Section 3.2, accounting for one-tenth of the original dataset. None of the images for the prediction evaluation in this section participated in the training process. The linear fusion method performs better than the other methods on its own test set and FISSURES dataset. Figure 13 shows the prediction results of the seven methods on our dataset. In the case of no interference, the segmentation integrity of SoUNet-fusion is better than that of other methods, and the noise produced is less than other results. In addition, our method performs well on rough asphalt pavement that is difficult to identify, and the segmentation results are relatively complete, but there are some false positive areas and a small number of noise points. Shadows and water stains are not misjudged as cracks, but the segmentation accuracy decreases, and the results are incomplete. Figure 14 shows the test results for the FISSURES dataset. The asphalt pavement in the FISSURES dataset was relatively flat, but the crack depth was shallow, and the width was narrow, so the noise of the segmentation result was relatively small.