A robust impurity detection and tracking code, able to generate large sets of dust tracks from tokamak camera footage, is presented. This machine learning–based code is tested with cameras from the Joint European Torus, Doublet-III-D, and Magnum-PSI and is able to generate dust tracks with a $65–100\%$ classification accuracy. Moreover, the number dust particles detected from a single camera shot can be up to the order of 1000. Several areas of improvement for the code are highlighted, such as generating more significant training data sets and accounting for selection biases. Although the code is tested with dust in single two-dimensional camera views, it could easily be applied to multiple-camera stereoscopic reconstruction or nondust impurities.

中文翻译：

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可靠的杂质检测和托卡马克跟踪

提出了一种鲁棒的杂质检测和跟踪代码，该代码能够从托卡马克相机镜头中生成大量尘埃轨迹。该基于机器学习的代码已通过Joint European Torus，Doublet-III-D和Magnum-PSI的相机进行了测试，并且能够使用$\mathrm{65岁}–100％$分类准确性。此外，从单次相机拍摄中检测到的灰尘颗粒数量最多可以达到1000个。突出显示了该代码的几个改进方面，例如生成了更重要的训练数据集并考虑了选择偏差。尽管该代码是在单个二维相机视图中用灰尘测试的，但可以轻松地将其应用于多相机立体重建或无灰尘杂质。