@inproceedings{sadallahCrackforwardContextAwareSeverityStage2026,

title = {Crackforward: Context-Aware Severity-Stage Crack Synthesis for Data Augmentation},

author = {N. Sadallah and M. S. Allili},

url = {https://www.scopus.com/pages/publications/105046224944?origin=resultslist},

doi = {10.1109/CCECE68150.2026.11610144},

isbn = {08407789 (ISSN); 979-833158840-3 (ISBN)},

year  = {2026},

date = {2026-01-01},

booktitle = {Can Conf Electr Comput Eng},

pages = {627–632},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Reliable crack segmentation is 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. © 2026 IEEE.},

note = {Journal Abbreviation: Can Conf Electr Comput Eng},

keywords = {Context-Aware, Crack Expansion, Crack morphology, Crack propagation, Crack segmentation, Crack segmentations, Data augmentation, Growth patterns, Health monitoring, Local Eigenvector Orientation, Morphology, Random processes, Structural health, Structural health monitoring, Textures, Unet-style generation},

pubstate = {published},

tppubtype = {inproceedings}

}