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

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 0.5 < th < 0.7, 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.