CW-HRNet: Constrained Deformable Sampling and Wavelet-Guided Enhancement for Lightweight Crack Segmentation

This paper presents CW-HRNet, a high-resolution, lightweight crack segmentation network designed to address challenges in complex scenes with slender, deformable, and blurred crack structures. The model incorporates two key modules: Constrained Deformable Convolution (CDC), which stabilizes geometric alignment by applying a tanh limiter and learnable scaling factor to the predicted offsets, and the Wavelet Frequency Enhancement Module (WFEM), which decomposes features using Haar wavelets to preserve low-frequency structures while enhancing high-frequency boundaries and textures. Evaluations on the CrackSeg9k benchmark demonstrate CW-HRNet’s superior performance, achieving 82.39% mIoU with only 7.49M parameters and 10.34 GFLOPs, outperforming HrSegNet-B48 by 1.83% in segmentation accuracy with minimal complexity overhead. The model also shows strong cross-dataset generalization, achieving 60.01% mIoU and 66.22% F1 on Asphalt3k without fine-tuning. These results highlight CW-HRNet’s favorable accuracy-efficiency trade-off for real-world crack segmentation tasks.

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