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This paper introduces CrackForward, a context-aware generative framework for synthesizing realistic crack growth patterns for data augmentation in structural health monitoring. The framework uses a contextually guided crack expansion module with adaptive random walks and a two-stage U-Net generator to model crack morphology, including thickness and branching. Experiments demonstrate that data augmented with CrackForward improves the performance of crack segmentation architectures by preserving target-stage saturation and thickness characteristics.
Synthetic cracks that respect real-world growth patterns can significantly boost the accuracy of crack detection models, offering a promising alternative to expensive manual annotation.
Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challenge. To address this limitation, we propose a novel context-aware generative framework designed to synthesize realistic crack growth patterns for data augmentation. Unlike existing methods that primarily manipulate textures or background content, CrackForward explicitly models crack morphology by combining directional crack elongation with learned thickening and branching. Our framework integrates two key innovations: (i) a contextually guided crack expansion module, which uses local directional cues and adaptive random walk to simulate realistic propagation paths; and (ii) a two-stage U-Net-style generator that learns to reproduce spatially varying crack characteristics such as thickness, branching, and growth. Experimental results show that the generated samples preserve target-stage saturation and thickness characteristics and improve the performance of several crack segmentation architectures. These results indicate that structure-aware synthetic crack generation can provide more informative training data than conventional augmentation alone.