CPF-UNet: A Dual-Path U-Net Structure for Semantic Segmentation of Panoramic Surround-View Images

Abstract

In this study, we propose a dual-stream UNet neural network architecture design named CPF-UNet, specifically designed for efficient semantic pixel-level segmentation tasks. This architecture cleverly extends the basic structure of the original UNet, mainly through the addition of a unique attention-guided branch in the encoder part, aiming to enhance the model’s ability to comprehensively capture and deeply fuse contextual information. The uniqueness of CPF-UNet lies in its dual-path mechanism, which differs from the dense connectivity strategy adopted in networks such as UNet++. The dual-path structure in this study can effectively integrate deep and shallow features without relying excessively on dense connections, achieving a balanced processing of image details and overall semantic information. Experiments have shown that CPF-UNet not only slightly surpasses the segmentation accuracy of UNet++, but also significantly reduces the number of model parameters, thereby improving inference efficiency. We conducted a detailed comparative analysis, evaluating the performance of CPF-UNet against existing UNet++ and other corresponding methods on the same benchmark. The results indicate that CPF-UNet achieves a more ideal balance between accuracy and parameter quantity, two key performance indicators.