Deep Learning-Based Characterization of Detonation-Cell Size Distributions in Soot-Foil Records
The geometric size and regularity of detonation cells are key physical parameters for characterizing detonation waves. Traditional manual measurement of soot foils is time-consuming and subjective, while existing computer vision techniques often exhibit poor generalization on real experimental images with high noise, blurred boundaries, and severe overlapping. To address this, we propose a novel method for automated recognition and high-order feature extraction of detonation cells based on deep learning instance segmentation (Mask R-CNN). By constructing a custom heterogeneous dataset (numerical simulations and physical experiments) and integrating transfer learning, the model achieves ac...
| Authors | Mingyang Bu, Robson A. Schneider, Karl P. Chatelain et al. |
|---|---|
| Primary category | eess.IV |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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