非对称的编码器-解码器架构下图像分割方法研究

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中图分类号：TP391.41 文献标志码：A文章编号：1006-0316（2025）06-0001-08

doi：10.3969/j.issn.1006-0316.2025.06.001

Abstract：Traditional image segmentation techniques often rely on deep learning models based on Convolutional Neural Networks and Transformer architectures.Although these models excel at local feature extraction，they have limitations in capturing long-range dependencies.Moreover，such models tend to have a large number of parameters and high computational complexity，which results in significant computational burdens，especially in resource-constrained environments.To address this issue，this paper proposesa lightweight image segmentation method based on Mamba.By introducing Mamba’s eficient architecture combined with the classical U-Net structure， this method aims to tackle the challenges faced by image segmentation models in mobile device scenarios， such as large parameter sizes and inadequate processing speeds for real-time applications.Specificaly，the method incorporates Visual State Space （VSS） blocks，which are used alongside convolutions （CNN） to form hybrid building blocks for capturing extensive contextual information.Additionally，a non-symmetric encoder-decoder structure is designed.Experimentsonthe public dataset ISIC20l7 show that， while maintaining segmentation accuracy，the proposed model reduces the parameter count by 99.94% compared to traditional pure visual Mamba models， by 75.51% compared to the lightest existing visual Mamba U-Net model， and by 99.84% compared to the classic U-Net model. The designed model achieves significant reductions in computational complexity while maintaining excellent segmentation accuracy， thus meeting the demands ofreal-time applications.

Key words ∵ Mamba; image segmentation;lightweight mode;encoder;decoder

随着深度学习在计算机视觉领域的广泛应用，图像分割技术在医疗图像分析、自动驾驶、遥感影像处理等领域得到了越来越多的关注和应用。（剩余8861字）