igor3357/shock_waves_segmentation

Shock Wave Segmentation Dataset

This dataset is designed for the segmentation of shock waves in experimental shadowgraph images. It contains images of unsteady gas-dynamic flows with polygon annotations marking visible shock-wave fronts. The annotations are provided in the Ultralytics YOLO segmentation format and are compatible with YOLO26.

The dataset can be used to train, validate, and evaluate models for automatic shock-wave detection and segmentation. Potential applications include extracting shock-front geometry, tracking shock-wave motion, measuring curvature and stand-off distance, and automating the processing of large datasets obtained using high-speed flow visualization.

Task: Instance segmentation Image type: Experimental shadowgraph images Class: Shock wave Annotation format: YOLO polygon segmentation Recommended model: Ultralytics YOLO26

Authors:

• Doroshchenko Igor
• Popov Pavel
• Moiseevskii Aleksei
• Kuli-Zade Tahir

Related Publications

The publications below provide scientific and methodological background for computer-vision analysis of shock waves and other gas-dynamic structures in experimental shadowgraph images. They should not be interpreted as descriptions of the exact dataset released in this repository.

If you use this dataset or the accompanying script in scientific work, please cite the dataset repository and one or both of the following publications, depending on the focus of your study.

Analysis of striped structures behind a shock wave

This article presents a computer-vision-based analysis of striped structures developing in the boundary layer behind a shock wave. It is particularly relevant to studies of shock-wave-induced unsteady flow structures and automated processing of high-speed flow-visualization data.

I. A. Znamenskaya, I. A. Doroshchenko, A. Kopylov, and T. A. Kuli-Zade, “Analysis of Striped Structures in the Boundary Layer Behind a Shock Wave Using Computer Vision Techniques,” Journal of Flow Visualization and Image Processing, vol. 33, no. 2, pp. 1–19, 2026. https://doi.org/10.1615/JFlowVisImageProc.2025058234

@article{znamenskaya2026stripedstructures,
  author  = {Znamenskaya, Irina A. and Doroshchenko, Igor A. and Kopylov, Anatoly and Kuli-Zade, Tahir A.},
  title   = {Analysis of Striped Structures in the Boundary Layer Behind a Shock Wave Using Computer Vision Techniques},
  journal = {Journal of Flow Visualization and Image Processing},
  year    = {2026},
  volume  = {33},
  number  = {2},
  pages   = {1--19},
  doi     = {10.1615/JFlowVisImageProc.2025058234},
  url     = {https://doi.org/10.1615/JFlowVisImageProc.2025058234}
}

Computer-vision analysis of experimental shadowgraph images

This article is the most directly relevant methodological reference for automatic analysis of shock waves and bow shocks in experimental shadowgraph images. It describes both classical computer-vision algorithms and YOLO-based deep learning for recognizing gas-dynamic structures.

I. A. Doroshchenko, “Analysis of the Experimental Flow Shadowgraph Images by Computer Vision Methods,” Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie), vol. 24, no. 2, pp. 231–242, 2023. https://doi.org/10.26089/NumMet.v24r217

@article{doroshchenko2023shadowgraph,
  author  = {Doroshchenko, Igor A.},
  title   = {Analysis of the Experimental Flow Shadowgraph Images by Computer Vision Methods},
  journal = {Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie)},
  year    = {2023},
  volume  = {24},
  number  = {2},
  pages   = {231--242},
  doi     = {10.26089/NumMet.v24r217},
  url     = {https://doi.org/10.26089/NumMet.v24r217}
}

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license: apache-2.0