关键词: 图像分割, UNet, 交互式卷积, 全局?局部特征交互, 特征融合

Abstract:

UNet has been widely applied to image segmentation tasks due to its encoder⁃decoder structure and skip⁃connection mechanism. However, traditional downsampling operations (such as max pooling or average pooling) tend to cause loss of spatial detail information during feature compression, while the simple reconstruction approach of the upsampling stage struggles to fully model global contextual semantics. These limitations constrain the network's expressive power and segmentation accuracy. To address this, this paper proposes an enhanced UNet architecture by introducing the Interactive Pooling Module (IPM) and the Global⁃Local Feature Interaction Module (GLFIM) during subsampling and upscaling stages, respectively, to strengthen feature modeling capabilities.Results indicate that in the encoder, the IPM simultaneously integrates max⁃pooling and average⁃pooling branches. Through channel interaction and feature fusion, it effectively enhances feature representation capabilities while preserving more structural and statistical information. In the decoder, the proposed Local Block extracts local details through residual convolutions, while the Global Block combines an attention mechanism based on adaptive pooling with an MLP network to model long⁃range dependencies and global semantic context. Experimental results demonstrate that our method exhibits significant advantages in preserving critical details and enhancing global semantic understanding, achieving superior segmentation performance compared to the standard UNet model. On the WHU dataset, IoU and F1 scores reached 90.73% and 95.14%, respectively.

Key words: Image segmentation, UNet, Interactive convolution, Global-local feature interaction, Feature fusion