# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 3 Results
### Segmentation Result  
The experimental data presented in Tables 1 and 2 strongly demonstrate that the proposed CPF-UNet model achieves excellent accuracy in image segmentation tasks. Its Mean Intersection over Union (MIoU) metric reaches 89.95%, representing a significant improvement compared to previous methods. Additionally, the Recall metric increases to 93.33%, demonstrating the outstanding performance of the model in segmenting the entire target region. Notably, despite the significant improvement of CPF-UNet over UNet, it still maintains a relatively low level of parameters. Compared to the UNet++ architecture, the method presented in this paper achieves a substantial performance enhancement with only a limited additional parameter cost (from 32.089 M to 36.202 M), as shown in Table 3. This comparison highlights the efficiency of CPF-UNet in balancing segmentation accuracy and model complexity. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **MIoU** & **Drivable Area** & **Wall** & **Static Vehicle** & **Parking Lines** & **Collision Avoidance** & **Speed Bumps** & **Arrows** \\ \hline UNet & 84.3 & 98.02 & 97.37 & 97.81 & 64.73 & 74.28 & 93.93 & 64.14 & 84.16 \\ UNet++ & 89.7 & 98.56 & 98.1 & 98.72 & 73.42 & 83.1 & 95.43 & 80.14 & 90.11 \\ CPF-UNet & 89.95 & 98.6 & 98.14 & 98.79 & 73.78 & 83.47 & 95.46 & 81.05 & 90.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The MIoU of different models on the SUPS dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **Recall** & **Drivable Area** & **Wall** & **Static Vehicle** & **Parking Lines** & **Lane Lines** & **Collision Avoidance** & **Speed Bumps** & **Arrows** \\ \hline UNet & 90.36 & 99.3 & 98.08 & 98.46 & 76.89 & 83.0 & 95.8 & 81.18 & 90.17 \\ UNet++ & 93.06 & 99.57 & 98.53 & 99.08 & 80.72 & 87.98 & 96.62 & 88.66 & 93.34 \\ CPF-UNet & 93.33 & 99.58 & 98.53 & 99.14 & 81.29 & 88.2 & 96.78 & 89.39 & 93.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The recall of different models on the SUPS dataset. As shown in Figure 4, the intuitive Visualization comparison further validates the significant improvement in the segmentation effect of CPF-UNet compared to the traditional UNet model, while also highlighting its subtle performance advantages in certain aspects compared to the advanced model UNet++. An analysis of the experimental results demonstrates that the proposed CPF-UNet model achieves significant precision advantages over the baseline UNet model in semantic segmentation tasks. This is evident in the network's capability to handle anti-aliasing at the edges of fisheye images, as illustrated in Figure 4 (with red arrows indicating the critical areas), where, when dealing with the distant parts of the image, CPF-UNet manages to segment out lane lines that are indiscernible with the UNet. This demonstrates that  
Figure 4: The first line shows the original BEV image, the second line indicates the ground truth after segmentation, the third line shows the inference result of UNet, and the last line shows the inference result of CPF-UNet. The difference between UNet and CPF-UNet is indicated by the red arrow in the figure. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **FLOPs (G)** & **Parameters (M)** \\ \hline UNet & 51.017 & 32.089 \\ UNet++ & 200.383 & 47.193 \\ CPF-UNet & 59.289 & 36.212 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The FLOPs and the parameters for various models. CPF-UNet possesses an improved contextual processing capability in comparison to UNet. Specifically, CPF-UNet has achieved a remarkable increase in the core metric of Mean Intersection over Union (MIoU) for evaluating segmentation performance, outperforming UNet by 5.65 % points. Simultaneously, the Recall rate has also increased by 2.97% points, reflecting the remarkable improvement in the model's ability to completely capture the target region.