# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results
### Experimental Evaluation
#### 3.5.2 CamVid Dataset Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network** & **Input Size** & **Speed/fps** & **Params/M** & **MIoU** & **FWIoU** & **GFLOPs** \\ \hline ALE [31] & 360 \(\times\) 480 & - & - & 53.6 & 62.1 & - \\ SuperParsing [32] & 360 \(\times\) 480 & - & - & 42.0 & 52.0 & - \\ FCN-ResNet34 & 360 \(\times\) 480 & 157.10 & 21.34 & 49.1 & 60.5 & 55.89 \\ ENet [33] & 360 \(\times\) 480 & 135.4 & 0.37 & 51.3 & 63.2 & 3.83 \\ SegNet [15] & 360 \(\times\) 480 & 46.18 & 29.45 & 55.6 & 66.9 & 104.54 \\ ESPNet [34] & 360 \(\times\) 480 & 132.00 & 0.36 & 55.6 & 67.2 & 3.31 \\ PSPNet-ResNet18 & 360 \(\times\) 480 & 25.98 & 46.64 & 57.1 & 68.8 & 123.69 \\ GAU [20] & 360 \(\times\) 480 & 38.15 & 7.94 & 64.5 & 72.3 & 81.64 \\ DeepLab-LFOV [4]] & 360 \(\times\) 480 & 122.53 & 15.31 & 61.6 & 69.3 & 125.00 \\ ICNet [35] & 360 \(\times\) 480 & 27.8 & 26.5 & 67.1 & 74.5 & 28.3 \\ DFANet A [36] & 360 \(\times\) 480 & 120 & 7.8 & 64.7 & 72.8 & 3.4 \\ DFANet B [36] & 360 \(\times\) 480 & 160 & 4.8 & 59.3 & 69.0 & 2.1 \\ BiSeNetV1\_X [37] & 720 \(\times\) 960 & 175.00 & 49.00 & 65.6 & 73.1 & 8.70 \\ BiSeNetV1\_R [37] & 720 \(\times\) 960 & 116.30 & 5.8 & 68.7 & 75.9 & 32.4 \\ JAUNet & 360 \(\times\) 480 & 35.23 & 33.91 & 66.3 & 74.2 & 137.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation metrics for each model on the CamVid dataset. slightly inferior to its segmentation results based on ResNet18 due to its inference speed and segmentation accuracy after using Xception39 or ResNet18 as the backbone network and pre-training on ImageNet, but because it needs to perform pre-training, JAUNet is slightly inferior to its segmentation results based on ResNet18. However, since JAUNet needs to perform pre-training, the computational cost is higher, and its model parametric number is also larger (up to 49 M) compared to other network models. JAUNet can achieve better segmentation accuracy with smaller input images than BiSeNet. Figure 9 also shows the variation of the test machine accuracy with the number of training rounds during training. Similar to the change of loss function during the training process, the mIoU of JAUNet on the test set rises slowly with the number of training rounds and stabilizes after 100 epochs. According to the change curve of mIoU, it can be found that JAUNet has a significant accuracy advantage on the test set compared to other U-shaped networks by virtue of the JAM attention module, which demonstrates the effectiveness of the JAM module. A comparison of the semantic segmentation results of the four networks on the CamVid dataset with the original and labeled images is shown in Figure 10. In the figure, images from left to right are the input images, and labeled images and segmentation results of each network are presented in order. As shown in Figure 10, where the first group of images has one vehicle target and several pedestrian targets, FCN can recognize pedestrians and vehicles, but the edge segmentation and that of the road are blurred. The UNet-GAU can recognize the pedestrian target on the left side, but mis-segmentation occurs in the building part on the right side. The second image shows multi-vehicle and multi-pedestrian targets, and each network can recognize the vehicle targets, but the pedestrian targets are not well recognized. UNet and UNet-GAU are not effective in the overlapping part of pedestrians and buildings on the left side, while the best segmentation for overlapping targets is JAUNet network and the worst segmentation is FCN. The third set of images are some continuous vehicle targets and a small pedestrian target, from which we can see that JAUNet can roughly identify the location of the traveler, while ordinary UNet is not able to accomplish it, with the appearance of a lot of noise. The fourth group of images is a bright scene of a bend and there are several vehicle targets in the distance. It can be seen that the networks can basically segment the bend contours well and the distant vehicle targets can be effectively identified.