Training defect detection algorithms for visual surface inspection systems requires a large and representative set of training data. Often there is not enough real data available which additionally cannot cover the variety of possible defects. Synthetic data generated by a synthetic visual surface inspection environment can overcome this problem. Therefore, a digital twin of the object is needed, whose micro-scale surface topography is modeled by texture synthesis models. We develop stochastic texture models for sandblasted and milled surfaces based on topography measurements of such surfaces. As the surface patterns differ significantly, we use separate modeling approaches for the two cases. Sandblasted surfaces are modeled by a combination of data-based texture synthesis methods that rely entirely on the measurements. In contrast, the model for milled surfaces is procedural and includes all process-related parameters known from the machine settings.

翻译：训练视觉表面检测系统的缺陷检测算法需要大量且具有代表性的训练数据。然而，通常可用的真实数据不足，且无法覆盖可能存在的各类缺陷。通过合成视觉表面检测环境生成的合成数据可以解决这一问题。为此，需要建立物体的数字孪生模型，其微观尺度表面形貌需通过纹理合成模型进行建模。我们基于喷砂和铣削表面的形貌测量数据，开发了针对这两种表面的随机纹理模型。由于表面图案差异显著，我们分别采用不同的建模方法：喷砂表面通过完全依赖测量数据的基于数据纹理合成方法进行建模；而铣削表面模型则采用过程化方法，涵盖了已知机器设置中的所有工艺相关参数。