Abstract
Accurate crack detection in concrete and pavement images is critical for infrastructure assessment but is limited by the scarcity of large, consistently annotated datasets. Supervised learning methods are particularly sensitive to data scarcity, often overfitting and generalizing poorly across crack types and imaging conditions. This study proposes a synthetic crack generation framework to augment or partially replace real datasets while reducing annotation effort. Synthetic cracks are generated by tracing minimal and maximal cumulative energy paths on random noise fields using dynamic programming, producing realistic one-pixel-wide crack strands. These are expanded via variable-width morphological dilation and deformed through geometric transformations and elastic deformation to model variations in width, tortuosity, and boundary irregularities across longitudinal, transverse, and shear cracks. The synthetic data trains a filter-based segmentation and connected component classification system rather than an end-to-end model. Over 2.25 million unique samples are generated across diverse scales and geometries. Elastic deformation increases geometric diversity, raising the mean normalized pairwise feature distance from approximately 0.17 to 0.31. Evaluation on Cracks-200, CDLN, and DeepCrack datasets shows performance comparable to human-annotated training data, with F1-scores up to 0.79 and mIoU exceeding 0.80. These results demonstrate that synthetic crack data can effectively supplement or substitute real annotated datasets, reducing annotation effort while preserving segmentation performance.