Deep learning model inspired by lateral line system for underwater object detection

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Inspired by the lateral line systems of various aquatic organisms that are capable of hydrodynamic imaging using ambient flow information, this study develops a deep learning-based object localization model that can detect the location of objects using flow information measured from a moving sensor array. In numerical simulations with the assumption of a potential flow, a two-dimensional hydrofoil navigates around four stationary cylinders in a uniform flow and obtains two types of sensory data during a simulation, namely flow velocity and pressure, from an array of sensors located on the surface of the hydrofoil. Several neural network models are constructed using the flow velocity and pressure data, and these are used to detect the positions of the hydrofoil and surrounding objects. The model based on a long short-term memory network, which is capable of learning order dependence in sequence prediction problems, outperforms the other models. The number of sensors is then optimized using feature selection techniques. This sensor optimization leads to a new object localization model that achieves impressive accuracy in predicting the locations of the hydrofoil and objects with only 40% of the sensors used in the original model.
Publisher
IOP PUBLISHING LTD
Issue Date
2022-01
Language
English
Article Type
Article
Citation

BIOINSPIRATION & BIOMIMETICS, v.17, no.2

ISSN
1748-3182
DOI
10.1088/1748-3190/ac3ec6
URI
http://hdl.handle.net/10203/292046
Appears in Collection
ME-Journal Papers(저널논문)
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