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

• Published in: Bioinspiration & biomimetics 2022-03, Vol.17 (2), p.26002
• Main Authors: Jeong, Taekyeong, Yoo, Janggon, Kim, Daegyoum
• Format: Article
• Language: English
• Subjects: Animals; Computer Simulation; Deep Learning; flow sensor; Hydrodynamics; Lateral Line System; Neural Networks, Computer; object detection; potential flow
• ISSN: 1748-3182; 1748-3190
• DOI: 10.1088/1748-3190/ac3ec6

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.