# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 4 Experiment
### Result on DeepGlobe Dataset  
In order to further evaluate the DCS-TransUperNet, we conducted a comparative experiment between our models and other SOTA road extraction methods on the DeepGlobe dataset, such as FCN-8s, U-Net, D-LinkNet, ELU-SegNet, and D-ResUnet. The experimental environment remains the same as previous experiments. Figure 7 illustrates a representative comparison result of road extraction on the DeepGlobe dataset from a globe view. There is more intensive road segmentation in the DeepGlobe dataset, which also increases the difficulty of road detection. Intuitively, our proposed result of road segmentation has better continuity and fewer false positives than others, such as the sub-regions marked by red boxes shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** & **IoU** & **Inference** \\ \hline DCS-TransUperNet-B & 81.63 & 77.42 & 79.46 & 64.22 & **2.01** \\ DCS-TransUperNet-L & **82.44** & **78.43** & **80.39** & **65.36** & 2.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative analysis results with different DCS-TransUperNet variants. Figure 6: Performance of DCS-TransUperNet with different \(\lambda\) values in the mixed loss function. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1-Score** & **IoU** & **Inference** \\ \hline CSwin TransU-Net & 81.01 & 77.04 & 78.97 & 64.05 & 1.98 \\ CSwin & 81.44 & 77.62 & 79.48 & 64.53 & 2.51 \\ TransUperNet & 80.79 & 76.91 & 78.80 & 63.78 & 3.27 \\ DCS-TransUperNet & **82.44** & **78.43** & **80.39** & **65.36** & **2.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative analysis results with different encoder, decoder and Dual-scales. Figure 8 shows more details. These three images are from the red box areas of Figure 7. The second column is the corresponding ground truth of this sub-region. The other columns are road segmentation results. From Figure 8A, there is obvious road false detection in (4), (5) and (6). Compared with (7) and (8), the segmentation result of (3) has better continuity and integrity. From Figure 8B, the road extraction results of (3), (4), and (6) are better than those of (5), (7), and (8). From Figure 8C, the overall segmentation result of (3) has made great progress, including continuity and accuracy compared with other methods. To sum up, our proposed model has strong road extraction ability. Table 4 shows the quantitative comparison results of different methods on the DeepGlobe dataset. DCS-TransUperNet with mixed loss achieved superior performance in precision (77.94), recall (69.52), F1-score (73.48) and IoU (56.74) in Table 5.  
Figure 8: Road segmentation results of subareas. (**A–C**) represent road segmentation results of subareas with different networks, respectively. (**1**) Input remote sensing image of the subareas. (**2**) The ground truth of the subareas. (**3**) Segmentation results with DCS-TransUperNet. (**4**) Segmentation results with D-ResUnet. (**5**) Segmentation results with ELU-SegNet. (**6**) Segmentation results with D-LinkNet. (**7**) Segmentation results with U-net. (**8**) Segmentation results with FCN-8s. Figure 7: Visual analysis of road segmentation results using different networks. (**1**) Testing remote sensing image. (**2**) The ground truth of this image. (**3**) Segmentation results with DCS-TransUperNet. (**4**) Segmentation results with D-ResUnet. (**5**) Segmentation results with ELU-SegNet. (**6**) Segmentation results with D-LinkNet. (**7**) Segmentation results with U-net. (**8**) Segmentation results with FCN-8s.