Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation
This work addresses crack segmentation for structural monitoring, which is important for infrastructure safety, but it appears incremental as it combines existing lightweight and dependency extraction techniques in a novel fusion approach.
The paper tackles the problem of pixel-level structural crack segmentation by proposing CrackSCF, a lightweight network that integrates local pattern recognition with long-range dependencies to address fragmentation and computational inefficiency in existing methods. The method achieved an F1 score of 0.8382 and mIoU of 0.8473 on the TUT dataset while using only 4.79M parameters.
Accurately segmenting structural cracks at the pixel level remains a major hurdle, as existing methods fail to integrate local textures with pixel dependencies, often leading to fragmented and incomplete predictions. Moreover, their high parameter counts and substantial computational demands hinder practical deployment on resource-constrained edge devices. To address these challenges, we propose CrackSCF, a Lightweight Cascaded Fusion Crack Segmentation Network designed to achieve robust crack segmentation with exceptional computational efficiency. We design a lightweight convolutional block (LRDS) to replace all standard convolutions. This approach efficiently captures local patterns while operating with a minimal computational footprint. For a holistic perception of crack structures, a lightweight Long-range Dependency Extractor (LDE) captures global dependencies. These are then intelligently unified with local patterns by our Staircase Cascaded Fusion Module (SCFM), ensuring the final segmentation maps are both seamless in continuity and rich in fine-grained detail. To comprehensively evaluate our method, this paper created the challenging TUT benchmark dataset and evaluated it alongside five other public datasets. The experimental results show that the CrackSCF method consistently outperforms the existing methods, and it demonstrates greater robustness in dealing with complex background noise. On the TUT dataset, CrackSCF achieved 0.8382 on F1 score and 0.8473 on mIoU, and it only required 4.79M parameters.