# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 5 Conclusions  
Inspired by the bilateral path mechanism adopted by BiSeNet, this study innovatively designed a dual-stream architecture to collaboratively enhance the basic performance of UNet. This architecture successfully mitigates the loss of spatial detail information caused by downsampling operations in deep networks by effectively integrating deep and shallow feature maps. In this study, we utilized the representative SUPS dataset to extensively train and compare multiple semantic segmentation models, including the newly proposed CPF-UNet model, as well as the UNet++ and baseline UNet models as control groups. The experimental results show that, compared to the original UNet, the proposed CPF-UNet achieves a significant improvement in the key metric of Mean Intersection over Union (MIoU), from 84.3 to 89.95, despite only a small increase in the parameter burden (from 32.089 M to 36.202 M). At the same time, compared to UNet++, CPF-UNet not only demonstrates a slightly higher segmentation accuracy, but also significantly reduces the total number of parameters required (the parameter size of UNet++ is 47.193 M), thereby significantly improving the model's inference efficiency while maintaining high accuracy. Overall, this dual-path architecture design concept possesses a certain degree of universality, making it especially suitable for network structures that may have deficiencies in capturing contextual information, and provides an effective solution for improving the performance of such models. In our experimental exploration, we discovered that the deep fusion of the third-layer feature map and the last-layer features of UNet plays a pivotal role in integrating shallow detail information with deep semantic information. Notably, by merely concatenating these two specific layers, we were able to achieve performance comparable to UNet++, while significantly reducing the number of model parameters. This innovative insight not only contributes to enhancing the model's operational efficiency, but also provides strong support for optimizing the model's inference speed. More broadly speaking, this strategy is also applicable to other densely connected neural network architectures. By leveraging this idea, we can further explore the potential key roles played by different layers in the network and conduct targeted network pruning operations, aiming to further accelerate inference speed and optimize overall performance. Dataset search and selection, F.Q. ; Network design, F.Q. ; Project administration, F.Q. ; Paper review, F.Q. ; Writing original draft, Q.S. ; Visualization, Q.S. ; Network construction, Q.S. ; Data analysis and interpretation, Q.S. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **MIou** & **Drivable** & **Static** & **Parking** & **Lane** & **Collision** & **Speed** & **Arrows** \\  & & **Area** & **Wall** & **Vehicle** & **Lines** & **Lines** & **Strips** & **Bumps** & **Arrows** \\ \hline UNet-f & 89.21 & 98.54 & 98.1 & 98.73 & 72.15 & 82.71 & 95.32 & 78.58 & 89.52 \\ UNet-cp & 89.20 & 98.56 & 98.14 & 98.85 & 71.51 & 82.67 & 95.26 & 79.17 & 89.44 \\ UNet-cat & 89.19 & 98.53 & 98.08 & 98.72 & 72.09 & 82.61 & 95.28 & 78.75 & 89.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Mean Intersection over Union (MIoU) in ablation studies of various methods under the same number of training epochs (Epochs). \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **Recall** & **Drivable** & **Wall** & **Static** & **Parking** & **Lane** & **Collision** & **Speed** & **Arrows** \\  & & **Area** & **Vehicle** & **Lines** & **Lines** & **Strips** & **Bumps** & **Arrows** \\ \hline UNet-f & 93.11 & 99.53 & 98.51 & 99.12 & 80.81 & 87.72 & 96.73 & 89.21 & 93.29 \\ UNet-cp & 93.07 & 99.54 & 98.51 & 99.16 & 80.51 & 87.81 & 96.64 & 89.17 & 93.23 \\ UNet-cat & 93.05 & 99.53 & 98.50 & 99.13 & 80.63 & 87.84 & 96.71 & 88.86 & 93.23 \\ \hline \hline \end{tabular}
\end{table}