生成逼真裂缝生长图像，提升结构损伤检测数据质量
CrackForward: Context-Aware Severity Stage Crack Synthesis for Data Augmentation
- 基于上下文引导的裂缝扩展模块，模拟真实裂缝蔓延路径
- 生成裂缝在不同阶段的厚度与分支特征，保持目标阶段饱和度
- 适合需要高质量裂缝数据的结构健康监测研究者
可靠的裂缝检测与分割对结构健康监测至关重要，但标注良好的数据稀缺是主要挑战。为此，我们提出一种新颖的上下文感知生成框架，用于合成真实的裂缝生长模式以实现数据增强。不同于以往仅修改纹理或背景内容的方法，CrackForward 明确建模裂缝形态，结合方向性延伸、学习得到的增厚与分叉机制。框架包含两项关键创新：(i) 上下文引导的裂缝扩展模块，利用局部方向线索与自适应随机游走模拟真实传播路径；(ii) 两阶段 U-Net 风格生成器，学习再现空间变化的裂缝特征，如厚度、分叉和生长模式。实验表明，生成样本保持目标阶段的饱和度与厚度特性，并提升了多个裂缝分割模型的性能。结果表明，结构感知的合成裂缝生成可提供比传统增强更富信息量的训练数据。
原文摘要 · Abstract (English)
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.
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