# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results
### Experimental Evaluation
#### 3.5.2 CamVid Dataset Results
The edges of the vehicle contours are shown in detail. The fifth group of pictures is an intersection scene. There is an intentional pedestrian target in the intersection and a pet in the hand of the pedestrian obscures the  
Figure 9: CamVid MIoU. road. It can be seen that JAUNet has the best segmentation effect on pedestrians and roads among the four networks. The last picture is a straight road scene and there is a line of people on the left side obscuring part of the vehicle. Among the four networks, FCN and JAUNet can identify the vehicle and pedestrian targets. FCN can recognize the situation that the traveler obscures the vehicle, but the segmentation of the contour is not good, and some mis-segmentation of the pedestrian's leg occurs in the segmentation result of JAUNet, and the segmentation results are good for other targets. The other networks are all inferior to JAUNet for overlapping targets, and UNet-GAU appears to mis-segment the pedestrian as a bicycle. Combining the above images, JAUNet has better results in edge segmentation and small targets in the CamVid dataset compared to other semantic segmentation networks. Details of the segmentation results for the CamVid dataset are shown in Figure 11. As can be seen, the differences in the segmentation results of the four networks are outlined in yellow areas in the figure. After looking closely at the yellow boxed part of the picture, we can see that the first set of images shows the noise that appears in the building and plant parts of UNet, SegNet, and JAUNet, and JAUNet performs the best. All three U-shape network structures can identify the travelers. However, some local missegmentation also occurs in all of them. JAUNet's mis-segmentation part is smaller, and the edges are more complete, while FCN fails to identify the traveler target at all and is not effective. The second group of images focuses on the segmentation details of the top of the vehicle. All three networks except FCN can segment the vehicle outline well while JAUNet has some small noise. The third group of images focuses on the segmentation details of the continuous vehicles on the left side. Both UNet and UNet-GAU have mis-segmentation of the background part. However,  
Figure 10: Segmentation results of each network on the CamVid dataset. JAUNet has better noise suppression and edge processing in this group of images. For the pedestrian target at the intersection in the fifth group of images, FCN identifies the pedestrian, but the edge segmentation is poor. UNet and UNet-GAU hardly identify the pedestrian, and mis-segmentation occurs. JAUNet can recognize pedestrian targets in the middle of the road and does not divide the pets of pedestrians into roads by mistake. The last set of images shows the details of the pedestrian obscuring the vehicle target. The FCN mis-segments the legs of pedestrians as vehicles, while the UNet and UNet-GAU mis-segment a portion of pedestrians as background. The JAUNet network can recognize the traveler and the vehicle. Although the part of the leg is mis-segmented into the vehicle, it is better than the other networks. To sum up, during the classification segmentation of CamVid dataset 11, FCN could not segment the object edges well, both UNet and SegNet show much noise in the background, and JAUNet segmentation results are relatively cleaner and had clear edges. In terms of visual effect, the JAUNet network outperforms other networks. Figure 11: Details of segmentation results for each network on the CamVid dataset.