## 4 Experiment
### Experimental Results
#### 4.3.2 Data Augmentation Experiment Results  
After data augmentation, we calculated the proportion of each type of point cloud, presented in Table 2.  
It can be seen from Table 2 that after data augmentation, the proportion of classes with fewer points is significantly improved, and it also provides a basis for training an efficient semantic segmentation network. To verify the effectiveness of the data augmentation module, we use the same parameters and the same network to train on the datasets before and after augmentation. IoU and mIoU are calculated, which are shown in Table 2.  
It can be seen from Table 3 that when linear interpolation is used for data augmentation, the network is more effective for semantic segmentation of classes with fewer point clouds. In Test A, the network trained on the enhanced training set improved by 5% on the IoU of \"muud\", and improved by 27% on the IoU of \"people\". In Test B, The IoU increased by 51% on \"muud\", and 36% on \"people\". Although the data augmentation leads to the decline of the IoU on some classes with a large number of point clouds, the decline is relatively low, with a maximum of no more than 4%, which does not affect the overall semantic segmentation accuracy, and the mIoU is still significantly improved. The experiments fully demonstrate that the data augmentation module can effectively improve the semantic segmentation accuracy of classes with a small number of point clouds.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{1}{c}{**Network**} & \multicolumn{5}{c}{**IoU (\%)**} & \multirow{2}{*}{**Time (ms)**} & \multirow{2}{*}{**Params (M)**} \\  & & & & & & & & & \\ \hline \multirow{3}{*}{A} & SalsaNext & 99.82 & 74.85 & 20.06 & 39.71 & 51.71 & 54.26 & 56.74 & **38** & 6.7 \\  & PVCNN & 99.56 & 82.35 & **24.19** & 41.93 & 56.54 & **57.04** & 60.35 & 151 & 2.5 \\  & Our work & **99.98** & **83.70** & 23.57 & **50.76** & **68.11** & 56.30 & **63.74** & 165 & 58.6 \\  & SalaSNext & 64.74 & 9.58 & 79.04 & 75.89 & 23.20 & 83.17 & 55.94 & **42** & 6.7 \\  & PVCNN & 63.58 & 13.27 & **86.79** & 82.93 & 27.65 & 84.63 & 59.81 & 152 & 2.5 \\  & Our work & **65.18** & **16.09** & 85.40 & **84.58** & **31.09** & **88.51** & **61.81** & 159 & 58.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Semantic segmentation results of different networks in Test A and Test B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Before** & **After** & **Change Rate** \\ \hline grass & 32.609\% & 30.874\% & \(-\)5.321\% \\ vegetation & 27.945\% & 26.458\% & \(-\)5.321\% \\ rocks & 22.204\% & 21.023\% & \(-\)5.319\% \\ water & 11.452\% & 10.843\% & \(-\)5.318\% \\ mud & 5.313\% & 9.922\% & 86.749\% \\ person & 0.211\% & 0.628\% & 197.630\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Changes of point clouds of each class after data augmentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**IoU (\%)**} \\  & **Grass** & **Mud** & **Vegetation** & **Rocks** & **Water** & **People** & **mIoU (\%)** \\ \hline \multirow{3}{*}{A} & before & 99.98 & 76.56 & 22.34 & **51.89** & 64.78 & 44.25 & 59.97 \\  & after & 99.98 & **83.70** & **23.57** & 50.76 & **68.11** & **56.30** & **63.74** \\ \cline{1-1}  & before & **65.40** & 10.67 & 82.07 & 81.99 & 30.61 & 64.94 & 55.95 \\ \cline{1-1}  & after & 65.18 & **16.09** & **85.40** & **84.58** & **31.09** & **88.51** & **61.81** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The IoU changes with the data augmentation module.