Existing crack recognition methods based on deep learning often face difficulties when detecting cracks in complex scenes such as brake marks, water marks, and shadows. The inadequate amount of available data can be primarily attributed to this factor. To address this issue, a controllable generative model of pavement cracks is proposed that can generate crack images in complex scenes by leveraging background images and crack mask images. The proposed model, the crack diffusion model (CDM), is based on the diffusion model network, which enables better control over the position and morphology of cracks by adjusting the conditional input of cracks. Experiments show that CDM has several advantages, including high definition, controllability, and sensitivity to narrow cracks. Utilizing CDM to create a synthetic crack data set in complex scenes resulted in substantial improvements of crack detection and segmentation. The method proposed in this study can effectively alleviate the effort required for data acquisition and labeling, especially in complex scenes.

中文翻译：

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复杂场景下路面裂缝图像生成的可控生成模型

现有的基于深度学习的裂缝识别方法在检测刹车痕、水痕、阴影等复杂场景中的裂缝时往往面临困难。可用数据量不足主要归因于这个因素。为了解决这个问题，提出了一种可控的路面裂缝生成模型，可以利用背景图像和裂缝掩模图像生成复杂场景中的裂缝图像。所提出的模型裂纹扩散模型（CDM）基于扩散模型网络，通过调整裂纹的条件输入，可以更好地控制裂纹的位置和形态。实验表明，CDM具有清晰度高、可控性强、对窄裂纹敏感等优点。利用 CDM 在复杂场景中创建合成裂纹数据集，极大地改进了裂纹检测和分割。本研究提出的方法可以有效减轻数据采集和标记所需的工作量，尤其是在复杂场景中。