Abstract: Fine-grained semantic segmentation plays an important role in obtaining accurate object boundaries. However, in real imaging scenarios, object edge regions often appear blurry, and insufficient modeling of edge features easily leads to inaccurate details in the segmentation results.Existing methods usually fail to pay sufficient attention to edge regions or rely on extra steps to extract edge information, which increases processing complexity. To this end, this paper proposes an edge-aware semantic segmentation network, EAM-UNet (Edge Aware Mamba-UNet). The network uses an improved Visual Mamba to capture long-range dependencies and reduces the computation of existing Visual Mamba modules through a bidirectional dilated selective scanning mechanism.It then uses a spatially guided dynamic upsampling module to dynamically control the upsampling process in edge regions and ensures accurate segmentation of edge details. Meanwhile, the network introduces an edge aggregation–aware module that extracts and aggregates edge features from semantic features and enhances representation on edge regions. Experimental results show that EAM-UNet performs excellently in scenarios that require high edge segmentation accuracy. On the medical image segmentation datasets ISIC 2017 and ISIC 2018, the method achieves mIoU of 82.52% and 84.07%, accurately depicting lesion boundaries and helping improve diagnostic reliability. On the industrial eyeglass-frame segmentation dataset GIS, the method achieves an mIoU of 98.37% and significantly improves the reliability of virtual try-on for frames. In addition, the method also outperforms existing approaches on Boundary IoU, a metric that focuses on edge segmentation quality.

摘要： 精细语义分割对于获取准确的目标边界具有重要作用。然而实际成像场景中，物体的边缘区域往往存在模糊现象，若缺乏对边缘特征的有效建模，容易导致分割结果在细节处不够准确。现有方法通常未能充分关注物体的边缘区域，或依赖额外步骤提取边缘信息，增加了处理的复杂性。为此，本文提出了一种边缘感知的语义分割网络EAM-UNet（Edge Aware Mamba-UNet），该网络利用改进的视觉Mamba捕捉长距离依赖关系并通过双向空洞选择性扫描机制降低现有视觉Mamba模块的计算量；再利用空域引导动态上采样模块对边缘区域上采样过程进行动态调控，确保边缘细节的准确分割；同时，设计边缘聚合感知模块从语义特征中提取并聚合边缘特征，增强模型对边缘区域的表达能力。实验结果表明，EAM-UNet在多个有高边缘分割精度要求的场景中表现优异。在医学图像分割数据集ISIC 2017与ISIC 2018中，该方法分别实现了82.52%与84.07%的mIoU，准确刻画了病灶边界，有助于提高诊断可靠性。在镜架分割的工业数据集GIS上，该方法的mIoU达到98.37%，显著提升了镜架虚拟试戴的可靠性。此外在专注于评估边缘分割质量的Boundary IoU指标上EAM-UNet同样优于现有方法。 关键词：语义分割；精细分割；双向空洞选择性扫