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http://dx.doi.org/10.3744/SNAK.2021.58.2.105

A Study on Autonomous Cavitation Image Recognition Using Deep Learning Technology  

Ji, Bahan (Department of Naval Architecture and Ocean Engineering, Chungnam National University)
Ahn, Byoung-Kwon (Department of Naval Architecture and Ocean Engineering, Chungnam National University)
Publication Information
Journal of the Society of Naval Architects of Korea / v.58, no.2, 2021 , pp. 105-111 More about this Journal
Abstract
The main source of underwater radiated noise of ships is cavitation generated by propeller blades. After the Cavitation Inception Speed (CIS), noise level at all frequencies increases severely. In determining the CIS, it is based on the results observed with the naked eye during the model test, however accuracy and consistency of CIS values are becoming practical issues. This study was carried out with the aim of developing a technology that can automatically recognize cavitation images using deep learning technique based on a Convolutional Neural Network (CNN). Model tests on a three-dimensional hydrofoil were conducted at a cavitation tunnel, and tip vortex cavitation was strictly observed using a high-speed camera to obtain analysis data. The results show that this technique can be used to quantitatively evaluate not only the CIS, but also the amount and rate of cavitation from recorded images.
Keywords
Tip Vortex Cavitation(TVC); Cavitation Inception Speed(CIS); Convolution Neural Network(CNN); Deep learning; Image recognition;
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Times Cited By KSCI : 1  (Citation Analysis)
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