MA-UNet model for crack segmentation in concrete structures utilizing Mamba skip connections in multiple scenarios

Concrete structures are prone to cracking during service, which, if left undetected, can lead to severe structural failure. Current deep learning paradigms face significant limitations: Convolutional Neural Networks (CNNs) are constrained by local receptive fields, hindering the capture of global continuity, while Transformer-based models suffer from quadratic computational complexity that impedes real-time deployment. To address these challenges, this paper proposes a concrete surface crack identification method utilizing a hybrid Mamba-attention segmentation model, termed MA-UNet. This model leverages the State Space Model (SSM) to achieve a global receptive field while maintaining linear computational overhead. Built upon the UNet architecture, the proposed framework introduces a novel Multi-head Depthwise-separable Channel Attention (MDCA) module. By integrating lightweight convolutions and a multi-scale feature fusion decoder, the model significantly enhances its capacity to suppress complex background noise. Experimental results demonstrate that MA-UNet outperforms conventional CNN and Transformer-based models across multiple datasets. Specifically, it achieves a maximum improvement of 13.83% in F1-score and 18.41% in mIoU, while reducing parameters by up to 119.58 M. Furthermore, it exhibits substantial advantages in key performance metrics such as FPS and FLOPs, offering a robust new technological trajectory for real-time, high-precision crack monitoring in sophisticated engineering environments.