Synergistic Integration of Cross-Spatial Learning for Lightweight Crack Detection

Abstract

Efficient crack segmentation is crucial for engineering surface inspection, especially on edge devices where both accuracy and computational efficiency are essential. To address the challenges posed by crack directionality and blurred edges while enhancing performance, we propose a lightweight segmentation model, SCCU-Net, based on cross-spatial synergistic learning and coordinate awareness. The model integrates coordinate information and perceptual sets into a hybrid attention mechanism, significantly boosting segmentation accuracy. We introduce a Mapping Attention Gate (MAG), which utilizes gating signals from fine-grained features to guide cross-spatial learning, alongside an Adaptive Skip Fusion (ASF) to ensure smooth feature integration while optimizing computational resources. Extensive experiments on six benchmark datasets demonstrate that SCCU-Net consistently outperforms existing lightweight models, setting a new benchmark for crack detection on edge devices. Code is available at https://github.com/lsyyy20000830/SCCU-Net.