Aero-engine combustion flame segmentation via Vision Transformer

Understanding and monitoring flame behavior in aero-engine combustors is critical for ensuring safe and efficient operation. The turbulent and high-pressure environment induces complex flame topologies and thermoacoustic instabilities that challenge conventional diagnostics. Traditional intensity-based thresholding methods are often inadequate for capturing coherent flame structures. In this study, we present the deep learning framework FlameSeg designed to improve segmentation accuracy and enable high-fidelity kinematic analysis of turbulent flames. FlameSeg integrates two key components: (1) hierarchical feature extraction with attention mechanisms to capture both fine boundary details and global contextual information, ensuring that subtle flame structures are preserved; and (2) a lightweight decoder with multi-scale feature fusion, which effectively integrates information across multiple resolutions, enabling precise delineation of flame contours and robust representation of overall flame structures. For validation, we established FlameDataset, a dedicated high-speed imaging collection from a representative aero-engine combustor. On this dataset, FlameSeg achieves a state-of-the-art mean Intersection over Union of 92.41%. The derived flame centroid trajectories attain an average mean absolute error of 8.07 pixels and a phase-averaged normalized error E_norm of 3.06% relative to the flame's characteristic diameter. This precision allows for the resolution of subtle kinematic variations often associated with the onset of combustion instabilities. These results demonstrate that FlameSeg constitutes a high-fidelity diagnostic framework for resolving turbulent flame dynamics, offering a robust pathway toward an improved understanding and monitoring of combustion instabilities.