AW-HRNet: A Lightweight High-Resolution Crack Segmentation Network Integrating Spatial Robustness and Frequency-Domain Enhancement

© 2025 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

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Abstract

The study presents AW-HRNet, a lightweight high-resolution crack segmentation network that couples Adaptive residual enhancement (AREM) in the spatial domain with Wavelet-based decomposition–reconstruction (WDRM) in the frequency domain. AREM introduces a learnable channel-wise scaling after standard 3 × 3 convolution and merges it through a residual path to stabilize crack-sensitive responses while suppressing noise. WDRM performs DWT to decouple LL/LH/HL/HH sub-bands, conducts lightweight cross-band fusion, and applies IDWT to restore detail-enhanced features, unifying global topology and boundary sharpness without deformable offsets. Integrated into a high-resolution backbone with auxiliary deep supervision, AW-HRNet attains 79.07% mIoU on CrackSeg9k with only 1.24M parameters and 0.73 GFLOPs, offering an excellent accuracy–efficiency trade-off and strong robustness for real-world deployment.

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