TY - JOUR

T1 - An Optimized Image-Processing Algorithm for Semi-Automated Measurement of Attached Cavities in High-Speed Flow Visualization

AU - Litvinova, Darya V.

AU - Zubairova, Ulyana S.

AU - Kravtsova, Aleksandra Yu

N1 - Litvinova, D.V.; Zubairova, U.S.; Kravtsova, A.Y. An Optimized Image-Processing Algorithm for Semi-Automated Measurement of Attached Cavities in High-Speed Flow Visualization. Sensors 2026, 26, 5166. https://doi.org/10.3390/s26165166 This research was funded by Russian Science Foundation, grant number 25-79-30002 (https://rscf.ru/project/25-79-30002/, accessed on 2 February 2026).

PY - 2026/8/14

Y1 - 2026/8/14

N2 - High-speed flow visualization provides imaging data containing quantitative information about cavitating-flow dynamics. Accurate determination of attached-cavity length is essential for characterizing cavitation regimes and validating mathematical models. In this study, an advanced image-processing algorithm for semi-automated analysis of cavitation patterns near hydrofoils is proposed. High-speed visualization data obtained for cavitating flow around a NACA0012 hydrofoil in a slit channel were used as input to the algorithm. The developed approach includes hydrofoil suppression, Otsu-based image binarization with threshold correction, filtering, and automated cavity-boundary detection. The initial search region for the cavity inception point is specified manually, whereas subsequent boundary tracking and cavity-length calculation are performed automatically. A dimensionless threshold correction coefficient was introduced to improve cavity identification, and its optimal range was determined. Additional geometric criteria were proposed to identify the cavity inception and closure locations and to separate attached cavities from detached vapor structures. The analysis showed that the optimal range of the threshold correction coefficient was (Formula presented.) < (Formula presented.) < (Formula presented.), while a geometric connectivity criterion based on a distance of 7 px between neighboring boundary pixels provided stable detection of the cavity closure location. The developed algorithm enables the determination of both instantaneous and time-averaged attached-cavity lengths, with a total estimated uncertainty not exceeding 3.5%. Comparison with previously published experimental and analytical data demonstrated good agreement and supported the reliability of the proposed approach. The method provides an explainable and training-free computer-vision pipeline that can potentially be adapted to other bluff-body geometries under comparable imaging and contrast conditions. It can also support automated annotation and the generation of reference datasets for the development and validation of future machine-learning methods for cavitation-flow analysis.

AB - High-speed flow visualization provides imaging data containing quantitative information about cavitating-flow dynamics. Accurate determination of attached-cavity length is essential for characterizing cavitation regimes and validating mathematical models. In this study, an advanced image-processing algorithm for semi-automated analysis of cavitation patterns near hydrofoils is proposed. High-speed visualization data obtained for cavitating flow around a NACA0012 hydrofoil in a slit channel were used as input to the algorithm. The developed approach includes hydrofoil suppression, Otsu-based image binarization with threshold correction, filtering, and automated cavity-boundary detection. The initial search region for the cavity inception point is specified manually, whereas subsequent boundary tracking and cavity-length calculation are performed automatically. A dimensionless threshold correction coefficient was introduced to improve cavity identification, and its optimal range was determined. Additional geometric criteria were proposed to identify the cavity inception and closure locations and to separate attached cavities from detached vapor structures. The analysis showed that the optimal range of the threshold correction coefficient was (Formula presented.) < (Formula presented.) < (Formula presented.), while a geometric connectivity criterion based on a distance of 7 px between neighboring boundary pixels provided stable detection of the cavity closure location. The developed algorithm enables the determination of both instantaneous and time-averaged attached-cavity lengths, with a total estimated uncertainty not exceeding 3.5%. Comparison with previously published experimental and analytical data demonstrated good agreement and supported the reliability of the proposed approach. The method provides an explainable and training-free computer-vision pipeline that can potentially be adapted to other bluff-body geometries under comparable imaging and contrast conditions. It can also support automated annotation and the generation of reference datasets for the development and validation of future machine-learning methods for cavitation-flow analysis.

KW - attached-cavity length

KW - cavitation

KW - computer vision

KW - edge detection

KW - high-speed imaging

KW - image processing

KW - image-based sensing

KW - optical sensing

KW - threshold correction factor

KW - uncertainty analysis

KW - компьютерное зрение

KW - анализ неопределенности

KW - кавитация

KW - обработка изображений

KW - высокоскоростная съемка

KW - поправочный коэффициент для порогового значения

KW - обнаружение границ

KW - длина присоединенной кавитационной каверны

KW - сенсорика на основе изображений

KW - оптические методы измерения

UR - https://www.mendeley.com/catalogue/4ebb2c11-4206-3100-b6e7-fcc440f61fda/

UR - https://www.scopus.com/pages/publications/105048687840

U2 - 10.3390/s26165166

DO - 10.3390/s26165166

M3 - Article

C2 - 42655473

VL - 26

JO - Sensors

JF - Sensors

SN - 1424-3210

IS - 16

M1 - 5166

ER -