# Encoder-Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
## 4 Frequency weighted intersection over union(FWIoU):
### Experimental Results for the Forest Scene  
We also conducted related experiments using two different forest scenes, which further demonstrated the effectiveness of our method. We first used the image labeling tool--labelme to manually label the forest images in the Make3D [43] dataset. The images were mainly divided into four categories, namely, blue trees, red sky, purple bushes and black background. A previous related work [25] was then used to predict the depth map of the forest image. Finally, the depth and RGB images were fused as the input for the semantic segmentation network.  
First, we conducted experiments using a public dataset containing images of the forest areas, and the detailed experimental results are shown in Figure 9. From Figure 9d,e, it can be seen that the result of semantic segmentations without depth information fusion produced large errors, while the segmentation results with depth information fusion were more accurate. In the area marked with the red box in the fourth row of Figure 9, a distant tree could be segmented using the RGB-D semantic segmentation network model.  
Second, we conducted experiments using 1k forestry images collected at the Beijing Olympic Forest Park. In fact, there were many trees in the forest scene, with a small DBH (diameter at breast height) and light color, and the branches were blocked from each other. When manually labeling, we only labeled the trunks in the foreground and divided the branches and trees in the background into the background without labeling. The experimental results are shown in Figure 10. The accuracy of our method for real forest scene segmentations was higher than that of the model without depth information.  
Furthermore, we evaluated our method using two different forest datasets since previous related works [11; 39; 40; 41] did not provide evaluation results on forest scenes. In order to compare fairly with relevant methods, we used the work by [PERSON] et al. [17] and our method to evaluate the results using the two different forest datasets. Table 3 shows a comparison of the results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **RGB–D** & **mIoU** \\ \hline FCN8s [11] & N & 65.3\% \\ DeepLabV2\(-\)CRF [39] & N & 70.4\% \\ ENet [40] & N & 58.3\% \\ ERFNet [41] & N & 65.8\% \\ ERF–PSPNet [41] & N & 64.1\% \\ SwiftNet [38] & N & 70.4\% \\ ARLoss [13] & N & 71.0\% \\ LDFNet [20] & G & 68.48\% \\ FFNet [16] & G & 72.5\% \\ ESOSD–Net [42] & P & 68.2\% \\ Our method & P & 73.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison of our results with results obtained using other methods for the Cityscapes dataset. “N” denotes methods without fusion depth information, “G” denotes methods with ground truth depth information and “P” denotes methods with predicted depth information.  
Figure 10: The visual effect on an actual forest scene. (**a**) Input images; (**b**) predicted depth maps by [26]; (**c**) ground truth images; (**d**) results without fused depth information; (**e**) our approach.  
Figure 9: The visual effect on a public dataset containing images of forest scenes. (**a**) Input images; (**b**) predicted depth maps by [25]; (**c**) ground truth images; (**d**) results without fused depth information; and (**e**) our approach.