Automated estimate of fish abundance through the autonomous imaging device GUARD1

Abstract Many technologies have been developed for monitoring the ocean interior. Among them the monitoring approaches based on imaging devices are capable to disclose important data on species behaviour and spatio-temporal variations of richness and evenness. In this context, the Argo programme ( http://doi.org/10.17882/42182 ) is a valuable instrument for monitoring the deep sea at global scale in space and time. Argo floats equipped with imaging devices are candidate to become a new monitoring tool for studying macro- and mega-fauna in large areas and for extended time periods, potentially providing monitoring results never attained before. This work summarises the results obtained on the automated fish recognition task performed on the images acquired by the GUARD1 imaging device 1 .

[1]  Paolo Menesatti,et al.  Challenges to the assessment of benthic populations and biodiversity as a result of rhythmic behaviour: Video solutions from cabled observatories , 2012 .

[2]  P. Poulain,et al.  Mediterranean intermediate circulation estimated from Argo data in 2003–2010 , 2010 .

[3]  Simone Marini,et al.  GUARD1: An autonomous system for gelatinous zooplankton image-based recognition , 2015, OCEANS 2015 - Genova.

[4]  Alvise Benetazzo,et al.  The 1966 Flooding of Venice: What Time Taught Us for the Future , 2016 .

[5]  Peng Wang,et al.  In situ glass antifouling using Pt nanoparticle coating for periodic electrolysis of seawater , 2015 .

[6]  Simone Marini,et al.  Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton , 2016, Sensors.

[7]  Boaz Zion,et al.  Review: The use of computer vision technologies in aquaculture - A review , 2012 .

[8]  Laurent Delauney,et al.  An Example: Biofouling Protection for Marine Environmental Sensors by Local Chlorination , 2008 .

[9]  Phil Culverhouse,et al.  Time to automate identification , 2010, Nature.

[10]  Christoph Waldmann,et al.  Societal need for improved understanding of climate change, anthropogenic impacts, and geo-hazard warning drive development of ocean observatories in European Seas , 2011 .

[11]  Brendan J. Godley,et al.  Camera technology for monitoring marine biodiversity and human impact , 2016 .

[12]  E. Ramirez-Llodra,et al.  An ecosystem-based deep-ocean strategy , 2017, Science.

[13]  Muhammad Imran Malik,et al.  Automatic fish species classification in underwater videos: exploiting pre-trained deep neural network models to compensate for limited labelled data , 2018 .

[14]  Thomas B. Moeslund,et al.  Introduction to Video and Image Processing: Building Real Systems and Applications , 2012 .

[15]  James F. Peters Foundations of Computer Vision - Computational Geometry, Visual Image Structures and Object Shape Detection , 2017, Intelligent Systems Reference Library.

[16]  C. C. Eriksen,et al.  Seaglider: a long-range autonomous underwater vehicle for oceanographic research , 2001 .

[17]  Changshui Zhang,et al.  DeepFish: Accurate underwater live fish recognition with a deep architecture , 2016, Neurocomputing.

[18]  J. J. Park,et al.  Deep currents obtained from Argo float trajectories in the Japan/East Sea , 2013 .

[19]  Simone Marini,et al.  A Novel, Unbiased Analysis Approach for Investigating Population Dynamics: A Case Study on Calanus finmarchicus and Its Decline in the North Sea , 2016, PloS one.

[20]  P. Favali,et al.  Coastal observatories for monitoring of fish behaviour and their responses to environmental changes , 2015, Reviews in Fish Biology and Fisheries.

[21]  A. Sterl,et al.  Fifteen years of ocean observations with the global Argo array , 2016 .

[22]  Ali M. Reza,et al.  Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement , 2004, J. VLSI Signal Process..

[23]  Jennifer M. Durden,et al.  A new method for ecological surveying of the abyss using autonomous underwater vehicle photography , 2014 .

[24]  Ana Cristina Cardoso,et al.  Assessing water ecosystem services for water resource management , 2016 .

[25]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[26]  P. Utgoff,et al.  RAPID: Research on Automated Plankton Identification , 2007 .