# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Comparison Experiments
#### 3.2.3 Results on the UAVid Dataset  
UAVid is an urban street view image dataset with a high resolution and large size, which is mainly collected by drones in different urban areas. As a result, complex lighting conditions and multiple angles pose a challenge to accurately segment it. As shown in Table 3, our model obtained the highest mIoU on this dataset while maintaining good segmentation accuracy for most subclasses.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **Backbone** & **Lowveg.** & **Tree** & **Car** & **Building** & **Impsurf.** & **MeanF1** & **OA** \\ \hline Fast-SCNN & – & 85.3 & 86.9 & 85.2 & 94.5 & 90.8 & 87.6 & 88.5 \\ ABCNet & ResNet18 & 87.5 & **89.3** & 95.6 & 96.7 & 93.6 & 92.6 & 91.2 \\ DANet & – & 85.6 & 87.8 & 84.5 & 95.3 & 91.0 & 88.9 & 89.1 \\ MANet & ResNet50 & 87.7 & 88.1 & 95.5 & 96.3 & 92.9 & 91.6 & 90.6 \\ EANet & ResNet18 & 84.5 & 85.6 & 95.2 & 95.5 & 92.1 & 90.5 & 88.7 \\ DC-Swin & Swin-Tiny & 88.1 & 88.4 & 96.2 & **97.3** & 93.5 & 92.9 & 91.4 \\ BANet & ResT-Lite & 87.5 & 88.9 & 96.1 & 96.8 & 93.1 & 92.3 & 91.0 \\ SwinUNet & Swin-Tiny & 87.8 & 88.3 & 95.9 & 96.5 & 93.2 & 92.4 & 91.3 \\ VPANet & ResNet50 & 86.9 & 86.4 & 95.3 & 96.2 & 91.5 & 90.8 & 90.1 \\ SegFormer & MiT-B1 & 87.3 & 87.9 & 95.8 & 96.3 & 92.4 & 91.1 & 90.7 \\ UNetFormer & ResNet18 & 87.6 & 88.7 & **96.8** & 96.9 & 93.4 & 92.6 & 91.2 \\ Our model & ResNet18 & **88.3** & **88.9** & 96.5 & 97.1 & **93.8** & **93.1** & **91.5** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative evaluation results on the Potsdam test set. The highest values are marked in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Models** & **Backbone** & **Building** & **Tree** & **Veg.** & **MovC.** & **StaC.** & **Road** & **Clutter** & **Human** & **mIoU** \\ \hline Fast-SCNN & – & 84.9 & 79.3 & 61.1 & 60.5 & 48.3 & 77.2 & 65.6 & 10.6 & 61.4 \\ ABCNet & ResNet18 & 86.5 & 79.7 & 63.3 & 69.5 & 48.3 & **81.8** & 67.2 & 14.1 & 63.6 \\ DANet & – & 85.6 & 78.6 & 61.7 & 59.8 & 47.5 & 77.4 & 64.7 & 9.4 & 60.8 \\ MANet & ResNet50 & 85.1 & 77.6 & 60.2 & 67.1 & 53.4 & 77.8 & 64.9 & 14.9 & 62.5 \\ A2 FPN & ResNet18 & 86.9 & 79.3 & 64.1 & 70.2 & 53.7 & 81.3 & 67.5 & 21.4 & 65.3 \\ EANet & ResNet18 & 87.1 & 79.5 & 63.4 & 69.4 & 53.2 & 80.8 & 67.2 & 21.6 & 64.9 \\ DC-Swin & Swin-Tiny & 87.3 & 80.6 & 63.4 & 73.1 & 56.6 & 80.9 & 66.5 & 30.8 & 67.3 \\ BANet & ResT-Lite & 85.3 & 78.7 & 62.4 & 69.5 & 52.9 & 80.5 & 66.9 & 21.2 & 64.5 \\ SwinUNet & Swin-Tiny & 85.2 & 79.5 & 61.5 & 70.6 & 51.8 & 79.8 & 65.7 & 22.6 & 64.3 \\ VPANet & ResNet50 & 86.3 & 78.8 & 63.4 & 70.8 & 54.1 & 80.3 & 66.9 & 20.5 & 64.7 \\ SegFormer & MiT-B1 & 86.4 & 79.7 & 62.5 & 72.6 & 52.7 & 80.2 & 66.5 & 28.3 & 66.1 \\ UNetFormer & ResNet18 & 87.5 & 80.4 & 63.6 & 73.4 & 56.5 & **81.3** & 68.3 & **31.3** & 67.6 \\ Our model & ResNet18 & **87.6** & **81.4** & **64.2** & **75.3** & **56.8** & 81.1 & **69.1** & 31.1 & **68.2** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation results on the UAVid test set. The highest values are marked in bold.  
Specifically, the mIoU was 4.6% and 3.7% higher than the CNN-based network ABCNet and Transformer-based network BANet, respectively. In addition, the accuracy of our method in the human class was close to the UNetFormer based on CNN encoding and Transformer decoding, while achieving the highest scores on the two important categories (moving car and static car). As shown in the red box in Figure 9, the visualization results on this challenging dataset can further prove the superiority of our model. Generally speaking, the extraction results of our method were purer and had better performance on most subclasses.