High-Frequency Refined Mamba with Snake Perception Attention for More Accurate Crack Segmentation

Cracks are vital warning signs to reflect the structural deterioration in concrete constructions and buildings. However, their diverse and complex morphologies make accurate segmentation challenging. Deep learning-based methods effectively alleviate the low accuracy of traditional methods, while they are limited by the receptive field and computational efficiency, resulting in suboptimal performance. To address this challenging problem, we propose a novel framework termed High-frequency Refined Mamba with Snake Perception Attention module (HFR-Mamba) for more accurate crack segmentation. HFR-Mamba effectively refines Mamba’s global dependency modeling by extracting frequency domain features and the attention mechanism. Specifically, HFR-Mamba consists of the High-frequency Refined Mamba encoder, the Snake Perception Attention (SPA) module, and the Multi-scale Feature Fusion decoder. The encoder uses Discrete Wavelet Transform (DWT) to extract high-frequency texture features and utilizes the Refined Visual State Space (RVSS) module to fuse spatial features and high-frequency components, which effectively refines the global modeling process of Mamba. The SPA module integrates snake convolutions with different directions to filter background noise from the encoder and highlight cracks for the decoder. For the decoder, it adopts a multi-scale feature fusion strategy and a strongly supervised approach to enhance decoding performance. Extensive experiments show HFR-Mamba achieves state-of-the-art performance in IoU, DSC, Recall, Accuracy, and Precision indicators with fewer parameters, validating its effectiveness in crack segmentation.