CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation
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 re...
| Authors | Nassim Sadallah, Mohand Saïd Allili |
|---|---|
| Primary category | cs.CV |
| Published | 21 April 2026 |
| Last updated at source | 23 April 2026 |
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