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Sensors 2016, 16(12), 2124; doi:10.3390/s16122124

Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

1
Institute of Marine Sciences (ISMAR) in La Spezia, National Research Council of Italy (CNR), Forte Santa Teresa, 19032 Pozzuolo di Lerici (SP), Italy
2
OnAir srl, Via Carlo Barabino 26/4B, 16129 Genova, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: José-Fernán Martínez
Received: 6 October 2016 / Revised: 5 December 2016 / Accepted: 5 December 2016 / Published: 14 December 2016
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Abstract

Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances. View Full-Text
Keywords: content-based image recognition; feature selection; gelatinous zooplankton; autonomous underwater imaging; GUARD1 content-based image recognition; feature selection; gelatinous zooplankton; autonomous underwater imaging; GUARD1
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Corgnati, L.; Marini, S.; Mazzei, L.; Ottaviani, E.; Aliani, S.; Conversi, A.; Griffa, A. Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton. Sensors 2016, 16, 2124.

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