A context-aware generative model synthesizes crack growth patterns with directional propagation and learned morphology to augment training data and improve crack segmentation performance.
Experiment Setup To validate our framework, we conducted experiments using the DeepCrack dataset [ 6], consisting of 537 im- ages
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CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation
A context-aware generative model synthesizes crack growth patterns with directional propagation and learned morphology to augment training data and improve crack segmentation performance.