## 3 Results and Discussion
### Visual Performance  
To more intuitively present the distress segmentation performance of the different models in complex scenes, we selected seven pavement distress and negative sample images containing interferences in the test set, some of which contained interference noise such as oil stains, shadows, and tree branches. Figure 5 shows the segmentation results of the eight algorithms. As shown in Figure 6, a visual inspection reveals that the proposed method outperforms the other methods in the task of the two-class semantic segmentation of multiple types of pavement lesions in different natural scenarios. The proposed method performs especially well in coping with different types and sizes of distress, varied illumination, and disturbances, with the most fine-grained and complete lesion extraction, with fewer false-positive predictions. From the prediction results of the different segmentation networks based on CNN structures, as shown in column 3 of Figure 6, SegNet only achieves coarse segmentation of the distress samples and is poor at handling the details and discontinuities. It also has difficulty extracting the planar regions of pits. In addition, from the segmentation results of PSPNet and DeepLabv3+ in columns 4-7 of Figure 6, it can be found that, as these segmentation networks use multi-scale pooling and cavity convoluti \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Dataset** & **Metric** & \begin{tabular}{c} **SegNet** \\ [87] \\ \end{tabular} & \begin{tabular}{c} **PSNet** \\ [88] \\ \end{tabular} & \begin{tabular}{c} **DeepLabv3+** \\ [89] \\ \end{tabular} & \begin{tabular}{c} **U-Net** \\ [31] \\ \end{tabular} & \begin{tabular}{c} **HRNet** \\ [60] \\ \end{tabular} & \begin{tabular}{c} **Swin-UNet** \\ [42] \\ \end{tabular} & \begin{tabular}{c} **SegFormer** \\ [44] \\ \end{tabular} & \
\begin{tabular}{c} **ISTD-** \\ **DisNet** \\ \end{tabular} \\ \hline \multirow{4}{*}{Attribute} & Backbone & V16 & MV2 & R-50 & MV2 & X & V16 & H-V2 & ST & MIT-B2 & MIT-B2 \\  & Input size & 416 \(\times\) 416 & 473 \(\times\) 473 & 473 \(\times\) 473 & 512 \(\times\) 512 & 512 \(\times\) 512 & 480 \(\times\) 480 & 224 \(\times\) 224 & 512 \(\times\) 512 & 512 \(\times\) 512 \\  & Par/M & 16.32 & **2.38** & 46.71 & 5.813 & 54.709 & 24.89 & 29.538 & 27.18 & 27.35 & 28.94 \\  & CC/GFLOPs & **601.78** & 5.28 & 118.43 & 52.87 & 166.841 & 450.602 & 79.915 & 52.56 & 113.427 & 176.331 \\  & Speed/FPS & 57.91 & **131.38** & 75.53 & 100.32 & 47.05 & 26.99 & 23.32 & 110.95 & 34.75 & 28.35 \\ \hline \multirow{2}{*}{ISTD-TE} & F1/\% & 71.55 & 89.64 & 83.04 & 88.68 & 89.44 & 92.03 & 92.72 & 89.11 & 94.23 & **95.51** \\  & _mloU_/\% & 60.4 & 82.27 & 73.55 & 81.03 & 81.13 & 85.96 & 87.07 & 81.64 & 89.49 & **91.67** \\ \hline ISTD- &
\begin{tabular}{c} F1/\% \\ _mloU_/\% \\ \end{tabular} & 71.44 & 73.61 & 74.57 & 81.98 & 79.25 & 85.91 & 85.07 & 79.75 & 87.14 & **89.24** \\  & _mloU_/\% & 57.05 & 63.5 & 65.00 & 72.66 & 67.5 & 77.5 & 76.43 & 70.12 & 79.12 & **81.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation on the ISTD-PDS7 validation and test sets. Bold indicates the best performing item. the receptive field to a certain extent, but it leads to a large amount of spatial information loss, which results in a significant decrease in the small crack detection performance. In contrast, U-Net (column 8 of Figure 6) and HRNet (column 9 of Figure 6), which take into account the fusion of multi-scale feature information, show an improved ability to restore the details of the distress and are more suitable for the task of the intensive prediction of pavement distress; however, the inference speed is reduced (Table 2). Columns 10, 11, and 12 of Figure 6 demonstrate the potential of the transformer backbone-based feature extraction network structure for the intensive prediction task.