Accurate quantification of pavement crack width plays a pivotal role in assessing structural integrity and guiding maintenance interventions. However, achieving precise crack width measurements presents significant challenges due to: (1) the complex, non-uniform morphology of crack boundaries, which limits the efficacy of conventional approaches, and (2) the demand for rapid measurement capabilities from arbitrary pixel locations to facilitate comprehensive pavement condition evaluation. To overcome these limitations, this study introduces a cascaded framework integrating Principal Component Analysis (PCA) and Robust PCA (RPCA) for efficient crack width extraction from digital images. The proposed methodology comprises three sequential stages: (1) initial crack segmentation using established detection algorithms to generate a binary representation, (2) determination of the primary orientation axis for quasi-parallel cracks through PCA, and (3) extraction of the Main Propagation Axis (MPA) for irregular crack geometries using RPCA. Comprehensive evaluations were conducted across three publicly available datasets, demonstrating that the proposed approach achieves superior performance in both computational efficiency and measurement accuracy compared to existing state-of-the-art techniques.

翻译：路面裂缝宽度的精确量化对于评估结构完整性和指导维护干预具有关键作用。然而，实现精确的裂缝宽度测量面临显著挑战，原因在于：（1）裂缝边界形态复杂且不均匀，限制了传统方法的有效性；（2）需要从任意像素位置进行快速测量的能力，以促进全面的路面状况评估。为克服这些局限，本研究提出一种集成主成分分析（PCA）与鲁棒主成分分析（RPCA）的级联框架，用于从数字图像中高效提取裂缝宽度。所提方法包含三个连续阶段：（1）使用现有检测算法进行初始裂缝分割以生成二值化表示；（2）通过PCA确定准平行裂缝的主取向轴；（3）利用RPCA提取不规则裂缝几何形态的主传播轴（MPA）。在三个公开数据集上进行的综合评估表明，与现有先进技术相比，所提方法在计算效率和测量精度方面均表现出更优的性能。