## 3 Results and Discussion
### Generalizability Analysis on Different Publicly Available Datasets  
In order to verify the generalizability of the ISTD-DisNet model for pavement distress segmentation, we chose the original SegFormer as the baseline model for comparison and calculated the distress segmentation metrics of the two algorithms for the CRACK500 [34], CFD [35], AigleRN [36], and GAPs384 [34] datasets, which come from different filming equipment and scenarios. As shown in Table 4, compared to the original SegFormer, the \(F1\) scores of the model proposed in this article are improved by 2.05%, 1.38%, 2.92%, and 2.19% on the four public test sets, and the mIoU scores are improved by 2.38%, 1.55%, 3.53%, and 2.23%, respectively. This shows that the model proposed in this article has superior robustness and generalizability. Although the mIoU of ISTD-DisNet on the public dataset is not higher than its score on the ISTD-CRTE test set, this is mainly because the labeling accuracy is not consistent.  
Figure 8: Loss curves during training of ISTD-DisNet: (**a**) the loss curve is amplified for the initial training phase, and (**b**) the loss curve at the end stage of the training is amplified.  
Figure 10 visualizes the segmentation results of the baseline and the model proposed in this article on the four datasets. Figure 10 shows the pavement crack images acquired by the different shooting devices, which have different resolutions, distress topologies, and texture noises. Figure 10 shows the corresponding labels of the original images. From Figure 10,d, it can be seen that, compared with the baseline model, the ISTD-DisNet crack segmentation model proposed in this article has a better degree of completeness and refinement, which also indicates that the use of the MAM attention mechanism and transposed convolution in the encoding stage can effectively solve the problem of the baseline model's insufficient fine-grainedness for fine cracks and improve the model's detail restoration ability. In addition, for the GAPs384 dataset captured with a line-array CCD camera, the method proposed in this article can also accurately segment the multiple types of distress in the images. The results of the quantitative and qualitative comparative analysis of the generalization show that the segmentation method proposed in this article can accurately segment most of the pavement crack-type lesions and that the model has strong robustness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **F1/\%** & _mIoU1/\%_ & **Equipment** & **Scenario** \\ \hline CFD & SegFormer & 82.65 & 73.98 & Smartphone & Urban roads in Beijing \\  & ISTD-DisNet & 84.70 & 76.36 & \\  & SegFormer & 81.66 & 72.72 & \\  & ISTD-DisNet & 83.05 & 74.27 & \\  & SegFormer & 71.53 & 62.45 & \\  & ISTD-DisNet & 74.45 & 65.98 & \\ GAPs384 & SegFormer & 70.46 & 61.91 & \\  & ISTD-DisNet & 72.65 & 64.14 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the crack segmentation results between ISTD-DisNet and SegFormer on four public datasets.  
Figure 9: Variation in the segmentation performance of ISTD-DisNet with different values of \(\lambda_{1}\):\(\lambda_{2}\).