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.

翻译：在像素级别准确分割结构裂缝仍是一个主要障碍，因为现有方法未能有效整合局部纹理与像素间依赖关系，常导致预测结果破碎且不完整。此外，其高参数量与巨大计算需求阻碍了在资源受限边缘设备上的实际部署。为应对这些挑战，我们提出了CrackSCF——一种轻量级级联融合裂缝分割网络，旨在以卓越的计算效率实现鲁棒的裂缝分割。我们设计了一个轻量级卷积块（LRDS）以替代所有标准卷积。该方法在极低计算开销下高效捕获局部模式。为全面感知裂缝结构，一个轻量级长程依赖性提取器（LDE）捕获全局依赖关系。随后，通过我们的阶梯级联融合模块（SCFM）将这些全局依赖与局部模式智能融合，确保最终分割图在连续性上无缝衔接且富含细粒度细节。为全面评估本方法，本文创建了具有挑战性的TUT基准数据集，并在其他五个公共数据集上进行了评估。实验结果表明，CrackSCF方法持续优于现有方法，且在处理复杂背景噪声时表现出更强的鲁棒性。在TUT数据集上，CrackSCF的F1分数达到0.8382，mIoU达到0.8473，且仅需4.79M参数。