CCMamba: A Multi-Head Criss-Cross Mamba for Pavement Crack Segmentation

Automatic and accurate detection of pavement cracks on highways and main roads is very important for intelligent pavement maintenance and traffic safety. However, due to the irregular shapes and uncertain characteristics of cracks, current methods face serious challenges on crack segmentation task. To solve the above issues, we propose a Multi-Head Criss-Cross Mamba (CCMamba) to efficiently enhance crack segmentation performance. Based on the sufficient details retained in low-level features and semantic information with strong discriminative ability in high-level features, CCMamba utilizes high-level features to extract crack information from low-level features. First, because of the feature representation capacity of latent state in State Space Model (SSM), we introduce a Multi-Head Latent State Module (Multi-Head LSM) to criss-cross study high-level local features and generate multiple Dynamic Convolution kernels. Second, these Dynamic Convolution kernels are applied to low-level features in a convolutional way, filtering crack information from massive background interferences. Third, the horizontal and vertical features output from Dynamic Convolution layers are fused with head attention mechanism, producing crack sensitive features. Finally, we use the obtained multi-scale features to predict segmentation masks. Comprehensive experiments on five public datasets, Crack500, GAPs384, CFD, CrackVision12K and CPRID, are conducted and our CCMamba achieves state-of-the-art (SOTA) performances compared to current crack segmentation methods. Meanwhile, ablation studies, visualization analysis and real scene testing also validate the effectiveness of CCMamba. Codes of this paper are public available at https://github.com/cv516Buaa/BinghaoLiu/tree/main/CCMamba