SDCrackSeg: A Frequency- and Spatial Geometry-Aware Topology-Preserving Network for Building Crack Segmentation

Crack segmentation on building surfaces is challenging due to the thin, curvilinear crack morphology and background interference from textures and illumination variations. This study proposes SDCrackSeg, a U-shaped network combining frequency-domain enhancement with geometry-adaptive convolution. The core Frequency Spatial Convolution module integrates two branches: Adaptive Frequency Convolution enhances high-frequency crack details, while Dynamic Snake Convolution adapts sampling to curvilinear structures. A topology-aware loss based on persistent homology further regularizes structural connectivity. Experiments on CHCrack5K demonstrate state-of-the-art performance with Precision 0.900, mIoU 0.816, F1-score 0.888, and Dice 0.675, while maintaining nearly 200 FPS inference speed. Results confirm that frequency–spatial fusion with topology regularization effectively improves crack detection reliability for practical building inspection.