# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results
### Experimental Evaluation
#### 3.5.2 CamVid Dataset Results  
We tested some networks to compare with JAUNet, and Figure 8 gives the change process of the loss function during the training of some networks. As shown in the figure, the losses of each network gradually converge after 100 rounds of training, and finally they all stabilize at about 0.04. Among them, UNet converges faster, while SegNet and JAUNet converge slightly slower. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Speed/fps** & **Params/M** & **MIoU** & **FWIoU** \\ \hline UNet [14] & **39.51** & **7.76** & 63.7 & 72.6 \\ UNet + ConvLSTM & 25.39 & 26.58 & 64.1 & 70.1 \\ UNet + 1 \(\times\) JAM & 38.95 & 9.34 & 63.9 & 72.5 \\ UNet + 2 \(\times\) JAM & 37.60 & 11.22 & 64.5 & 72.1 \\ UNet + 3 \(\times\) JAM & 36.34 & 15.31 & 65.5 & 73.1 \\ UNet + 4 \(\times\) JAM & 31.22 & 18.50 & 65.3 & 72.8 \\ UNet + 3 \(\times\) JAM + ConvLSTM & 35.23 & 33.91 & **66.3** & **74.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ablation experiments for JAM module and ConvLSTM module. The bolded part indicates the best performance of each indicator at this parameter. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\kappa}\) & \(\mathbf{\beta}\) & **PA** & **MPA** & **FWIoU** \\ \hline
1 & 0 & 80.7 & 64.4 & 72.3 \\
0.8 & 0.2 & 82.5 & 64.2 & 74.0 \\
**0.6** & **0.4** & **83.0** & **63.1** & **74.2** \\
0.4 & 0.6 & 79.3 & 64.2 & 70.5 \\
0.2 & 0.8 & 77.3 & 64.5 & 69.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of different weight loss functions on segmentation performance. The bolded part indicates the best performance of each indicator at this parameter. The objective evaluation metrics of the four models on the CamVid dataset are shown in Table 5.  
As shown in Table 5, compared with other types of network structures, JAUNet has advantages over most other network structures with a fixed input size of 360 \(\times\) 480. Compared with the basic semantic segmentation network structure of FCN, JAUNet also achieves excellent segmentation results owing to the encoder-decoder network structure. Compared with SegNet and UNet, which are also encoder-decoder networks, the segmentation accuracy of JAUNet is significantly improved, and its edge segmentation results for small targets are even better due to the cross-channel feature fusion mechanism of JAM. Compared with small networks such as ENet and ESPNet, JAUNet has significantly better segmentation accuracy, although it is inferior to such networks in terms of inference speed and model complexity. Compared with DeepLab, which uses the same basic network structure, JAUNet also has an obvious advantage in segmentation accuracy. Compared with ICNet and DFANet, which are newer networks, JAUNet is more balanced in terms of accuracy and speed. For a large network structure such as PSPNet, JAUNet not only has an advantage in segmentation accuracy but also has a certain advantage in inference speed. Meanwhile, compared with BiSeNetV1 with an input size of 720 \(\times\) 960, JAUNet is  
Figure 8: CamVid train loss.