# CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images
## 1 Introduction
### Related Work  
In the field of semantic segmentation, dual-path structures and encoder-decoder architectures represent two major trends in network design. Dual-path structures achieve the goal of maintaining segmentation accuracy while improving computational efficiency by processing feature information at different resolutions in parallel. For instance, the BiSeNet [19] proposed by [PERSON] et al. utilizes a dual-path structure to extract detailed features at high resolution and semantic features at low resolution, balancing high-speed inference with high-precision segmentation.  
Encoder-decoder architectures are another effective approach for semantic segmentation network design. The Fully Convolutional Network (FCN) [20] proposed by [PERSON] et al. marked a milestone in this architecture, paving the way for the application of deep learning in semantic segmentation. Subsequently, [PERSON] et al. introduced UNet [11], which features a symmetric encoder-decoder U-shaped structure and significantly improves segmentation accuracy through skip connections that effectively integrate features from different levels. To further enhance the performance of UNet, [PERSON] et al. proposed UNet++ [12], featuring denser skip connections that enable deep fusion of spatial information across scales, surpassing the original UNet. With the development of Transformers in computer vision, more Transformer-based models have been integrated into UNet. [PERSON] et al. presented TransUNet [14], a novel model combining Transformers with the UNet structure, tailored for medical image segmentation. This addresses the limitations of existing models in modeling long-range dependencies with local convolutions and in capturing low-level details with Transformer architectures. [PERSON] et al. introduced Swin-UNet [15], a new deep learning architecture based on Transformers for medical image segmentation tasks. This model, which is a pure Transformer architecture, adopts a UNet-like encoder-decoder design and utilizes skip connections to mitigate the loss of spatial information during downsampling. [PERSON] et al. proposed Swin-UNet++ [16], a model that combines the advantages of the Transformer architecture and UNet++, aiming to better capture long-range dependencies in images. [PERSON] et al. [7] proposed a hybrid neural network architecture combining CNN, LSTM networks, and Transpose CNNs for city-wide traffic congestion prediction. Their work demonstrates the effectiveness of integrating different neural network components to handle complex prediction tasks. This underscores the potential for innovative neural network architectures to significantly improve task-specific performance. However, as network structures become more complex, the number of model parameters and computational requirements also increase, limiting the deployment of these models in practical applications. To overcome this challenge, this study proposes a novel dual-path UNet structure, inspired by the theoretical achievements of dual-path structures [18; 19; 21; 22; 23; 24; 25; 26] and attention mechanisms [27]. This structure combines the encoder-decoder backbone of UNet with the characteristics of dual-path structures, and introduces attention mechanisms and feature fusion modules to achieve the goal of maintaining high segmentation accuracy while reducing the number of model parameters. Experiments on the SUPS [28] dataset have validated the practicality and significant effects of this approach, demonstrating a slight improvement in segmentation accuracy while reducing the number of parameters by up to 23.27%. This achievement provides new ideas and methods for research in the field of semantic segmentation.