Application of machine learning for detecting and tracking turbulent structures in plasma fusion devices using ultra fast imaging.

This study explores the application of machine learning techniques for detecting and tracking plasma filaments around the boundary of magnetically confined tokamak plasmas. Plasma filaments, also called blobs, are responsible for enhanced turbulent transport across magnetic field lines, and their accurate characterization is crucial for optimizing the performance of magnetic fusion devices. We present a novel approach that combines machine learning methods applied to data obtained from ultra-fast cameras, including YOLO (You Only Look Once) for object detection, semantic segmentation, and specific tracking methods. This approach enables fast and accurate detection and tracking of filaments while overcoming the limitations of conventional methods, which are time-consuming and prone to human subjectivity. A significant advance in our study lies in the development of a method for automatically labeling a large batch of data, which greatly facilitates the training of supervised machine learning algorithms. Using these techniques, we obtained promising results demonstrating a significant improvement over conventional tracking methods, achieving a detection accuracy of up to 98.8%, while reducing the inference time per frame by 15% to 31% compared to conventional Kalman filter tracking. These results open up new perspectives for investigating turbulent phenomena in tokamaks, and could have important implications for the development of controlled nuclear fusion.