ABSTRACT In this paper, we present a fully automatic evaluation approach that can be used for fast inline CT scanning. In contrast to classical defect-recognition algorithms, this approach does not require good image quality. Instead, it allows to distinguish CT artifacts from real defects introduced in the production process. To this end, a three-step workflow was developed, in which any deviations from a reference part are detected and subsequently classified and segmented, to allow automatic decision whether the part fulfills given quality requirements. We demonstrate this approach using CT scans of aluminum castings, typically used in the automotive industry. Also, a comparison between two different segmentation algorithms is shown.

中文翻译：

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用于快速评估真实在线 CT 扫描的自动缺陷检测

摘要 在本文中，我们提出了一种全自动评估方法，可用于快速在线 CT 扫描。与经典的缺陷识别算法相比，这种方法不需要良好的图像质量。相反，它允许将 CT 伪影与生产过程中引入的真实缺陷区分开来。为此，开发了一个三步工作流程，其中检测与参考零件的任何偏差，然后进行分类和分割，以允许自动决定零件是否满足给定的质量要求。我们使用通常用于汽车行业的铝铸件 CT 扫描来演示这种方法。此外，还显示了两种不同分割算法之间的比较。