An underwater electrosensor for identifying objects of similar volume and aspect ratio using convolutional neural network

Abstract

© 2017 IEEE. Underwater electrosense is bio-inspired by weakly electric fishes that use an electric field to see the objects in the water. Current studies on engineering electrosense focus on designing sophisticated sensors and algorithms for emulating biological functions including localization and identification. This work aimed to develop a planar sensor equipped with a dense electrode array that is capable of providing accurate and dense data for identifying objects of similar volume and aspect ratio, which has been a challenge in underwater sensing. After sensor design and implementation were presented, convolutional neural networks (CNN), which are widely used in digital image recognition, was trained using both simulation and experimental data. In the simulation, the overall success rate on identifying the sphere, cube, and rod is 92.6% by a 28 × 28 electrode array. In the preliminary experimental tests, a sensor with 16 × 16 electrode array achieved an overall success rate of 90.4% on identifying a sphere and a rod.