## 3 Results
### Ablation Experiments
#### 3.3.2 SCP-Net  
An important aspect of our study is the use of SCP-Net to correct the results of SAM segmentation to improve accuracy. To test the impact of this module on UGSAM performance, we designed an ablation experiment. We tested among FCN, PointNet++, and the UGSAM, where the experimental group removes the SCP-Net and the control group retains the SCP-Net, to analyze the pattern of change in model performance. The data used for the test consisted of normal weather street scenes and adverse weather street scenes in a ratio of 2:1.  
Table 8 shows the experimental results for different models and schemes. We conducted four sets of experiments, and the experimental results show that after using SCP-Net for correction, the OA of FCN is improved by 0.58%, the mIoU is improved by 0.47%, and both recall and precision are improved. The OA of PointNet++ is improved by 0.94%, mIoU is improved by 1.58%, and recall and precision are improved significantly. The OA of the UGSAM is improved by 1.04%, mIoU is improved by 0.61%, and recall and precision are also improved significantly. The experimental results show that the addition of SCP-Net helps to correct the image segmentation results and improve the segmentation accuracy.  
Another key innovation of SCP-Net lies in its multicomponent loss function, which combines cross-entropy (CE) and Dice loss. To investigate the influence of different loss functions on the performance of the UGSAM, a second ablation study was designed. Four experimental groups were established: Group 1 employed focal loss, Group 2 used standalone CE loss, Group 3 utilized standalone Dice loss, and Group 4 integrated all three loss functions. The control group replicated the original methodology, combining CE and Dice loss. The data used for the test consisted of normal weather street scenes and adverse weather street scenes in a ratio of 2:1.  
Table 9 presents the experimental results for different models and configurations. It can be observed that when using the combination of CE loss and Dice loss, PointNet++ achieves the best performance across all four metrics. FCN and the UGSAM achieve the best results in OA, recall, and mIoU, but their precision is slightly lower than that of the combination of all loss functions.  
Among the two metrics, recall and precision, recall reflects the proportion of actual positive samples that are correctly predicted, while precision focuses on the proportion of predicted positive samples that are actually positive. We prioritize models with higher recall because, in large-scale data, it is extremely difficult to manually identify vegetation areas that are not detected, whereas identifying errors in already segmented regions is easier due to the smaller area that needs to be checked.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **SCP-Net** & **OA** & **Recall** & **Precision** & **mIoU** \\ \hline FCN & ✗ & 0.6877 & 0.7961 & 0.7757 & 0.6981 \\ FCN & ✓ & **0.6935** & **0.8038** & **0.7878** & **0.7028** \\ \hline PointNet++ & ✗ & 0.7468 & 0.7554 & 0.8188 & 0.6831 \\ PointNet++ & ✓ & **0.7562** & **0.7665** & **0.8242** & **0.6989** \\ \hline UGSAM & ✗ & 0.8528 & 0.9166 & 0.8980 & 0.8391 \\ UGSAM & ✓ & **0.8632** & **0.9277** & **0.9048** & **0.8452** \\ \hline \hline \end{tabular}  
* The best results are highlighted in bold.  
\end{table}
Table 8: Effects of SCP-Net on segmentation accuracy.