# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 4 Discussion  
The results indicate that the improved RandLA-Net significantly outperformed the other two networks in terms of its overall segmentation performance. The overall prediction accuracy on the two test sets was 87.68% and 89.61%, respectively, representing an \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Network Model** & **Metrics (\%)** & **Road Surface** & \
\begin{tabular}{c} **High** \\ **Vegetation** \\ \end{tabular} &
\begin{tabular}{c} **Low** \\ **Vegetation** \\ \end{tabular} & **Sidewalk** & **Guardrail** & **Pole** \\ \hline \multirow{4}{*}{PointNet++} & Accuracy & 86.65 & 84.99 & 58.96 & 67.34 & 70.93 & 66.23 \\  & Recall & 86.28 & 77.31 & 67.05 & 84.80 & 84.82 & 66.27 \\  & F1 Score & 86.47 & 80.97 & 62.75 & 60.43 & 77.25 & 66.25 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 78.34 & & & \\  & Avg. F1 & & & 72.35 & & & \\ \hline \multirow{4}{*}{RandLA-Net} & Accuracy & 91.22 & 82.65 & 66.02 & 66.77 & 89.23 & 81.94 \\  & Recall & 93.55 & 82.65 & 66.02 & 66.77 & 89.23 & 81.94 \\  & F1 Score & 92.37 & 84.47 & 64.64 & 70.52 & 87.78 & 86.73 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 85.28 & & & \\  & Avg. F1 & & & 81.09 & & & \\ \hline \multirow{4}{*}{Ours} & Accuracy & 97.79 & 88.43 & 64.33 & 76.18 & 87.27 & 92.69 \\  & Recall & 95.38 & 82.78 & 70.08 & 72.44 & 90.25 & 80.01 \\ \cline{2-8}  & F1 Score & 95.09 & 85.51 & 67.08 & 74.26 & 88.73 & 85.88 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 87.68 & & & \\ \cline{2-8}  & Avg. F1 & & & 82.76 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Semantic segmentation results for the planar intersection point cloud (Test Set 1). Figure 11: Visual segmentation results for the road plane intersection point cloud. (**a**) Dataset 1. (**b**) Dataset 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Network Model** & **Metrics (\%)** & **Road Surface** & \
\begin{tabular}{c} **High** \\ **Vegetation** \\ \end{tabular} &
\begin{tabular}{c} **Low** \\ **Vegetation** \\ \end{tabular} & **Sidewalk** & **Guardrail** & **Pole** \\ \hline \multirow{4}{*}{PointNet++} & Accuracy & 87.02 & 83.10 & 60.56 & 67.60 & 77.66 & 51.86 \\  & Recall & 93.26 & 79.65 & 68.46 & 38.48 & 47.17 & 49.01 \\  & F1 Score & 90.03 & 83.10 & 60.56 & 67.60 & 77.66 & 51.86 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 81.28 & & \\  & Avg. F1 & & & 65.63 & & & \\ \hline \multirow{4}{*}{RandLA-Net} & Accuracy & 90.59 & 89.17 & 64.95 & 77.58 & 89.08 & 86.10 \\  & Recall & 94.26 & 86.76 & 66.41 & 55.10 & 74.17 & 80.35 \\ \cline{2-8}  & F1 Score & 92.39 & 87.95 & 65.67 & 64.44 & 80.95 & 83.12 \\ \cline{2-8}  & Overall Accuracy (OA) & & & 86.76 & & & \\  & Avg. F1 & & & 79.09 & & & \\ \hline \multirow{4}{*}{Ours} & Accuracy & 94.48 & 90.37 & 65.57 & 78.84 & 89.17 & 86.14 \\  & Recall & 96.81 & 86.77 & 75.01 & 55.31 & 74.21 & 81.04 \\ \cline{1-1}  & F1 Score & 95.63 & 88.53 & 69.97 & 65.01 & 81.00 & 83.51 \\ \cline{1-1} \cline{2-8}  & Overall Accuracy (OA) & & & 89.61 & & \\ \cline{1-1} \cline{2-8}  & Avg. F1 & & & 80.61 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Semantic segmentation results for the planar intersection point cloud (Test Set 2). improvement of 2.40% and 2.85% over the original network. The average F1 scores were 80.61% and 82.76%, respectively, indicating improvements of 1.67% and 1.52% compared to the original network. Due to the large input sample size and distinct geometric structures of the road surface areas, their prediction F1 scores reached 95.09% and 95.63%, respectively. The recognition effects for high vegetation, guardrails, and pole-like objects were also relatively good. The point cloud segmentation visualization results show that this method improved the ability to discriminate between different categories of point clouds. The main reason for this improvement is that the feature aggregation module excluded neighborhood points near the central sampling point with significant feature information differences, thereby preventing the network from learning redundant information.