Abstract: Cracks represent a common defect in building structures, making their timely and accurate detection crucial for ensuring structural safety. However, due to factors such as the slender and irregular morphology of cracks and interference from complex backgrounds, existing Convolutional Neural Network (CNN)-based methods are often limited by their local receptive fields and struggle to effectively handle cluttered foreground and background interference. To address this, a novel model combining CNN with the Mamba architecture was proposed, featuring a decoupled dual-branch encoder. This design leverages the complementary fusion of a local fine-grained feature extraction branch and a global context modeling branch, balancing detailed preservation with global perception capabilities. Furthermore, to mitigate the semantic discrepancies between the branches, a Frequency-guided Feature Fusion Module (FGFM) was designed. This module facilitates the fusion of high- and low-frequency features within the frequency domain, thereby enhancing the complementarity and expressive efficiency of local and global features. Experiments conducted on three public datasets (Volker, DeepCrack, and CFD) demonstrate that the proposed method outperforms several mainstream models, achieving F1-scores of 83.32%, 87.41%, and 74.86% respectively, validating its superior performance.

Key words: Crack Segmentation, Dual-Branch Encoder Architecture, Dense Attention Module, Frequency-Domain Feature Fusion, Mamba Architecture

摘要： 摘 要: 裂缝是建筑结构中的常见病害，及时、准确地检测裂缝对保障结构安全具有重要意义。然而，由于裂缝细长、形态不规则及复杂背景干扰等因素影响，现有基于卷积神经网络（CNN）的方法受限于局部感受野，难以有效应对杂乱的前景与背景干扰。为此，本文提出一种结合CNN与Mamba架构的解耦双分支编码模型，通过局部细粒度特征提取分支与全局上下文建模分支的互补融合，兼顾细节保持与全局感知能力。此外，针对分支间存在的语义差异，设计了频域引导特征融合模块（Frequency-guided Feature Fusion Module, FGFM），通过在频率域中引导高、低频特征融合，进一步提升局部与全局特征的互补性与表达效率。在Volker、DeepCrack和CFD三个公开数据集上的实验表明，本文方法优于多个主流模型，F1得分指标分别达到83.32%、87.41%和74.86%，验证了其优越性能。

关键词: 裂缝分割, 双分支编码结构, 密集注意模块, 频域特征融合, Mamba架构