Reconocimiento automático de especies utilizando procesamiento digital de imágenes acústicas

Accurate identification of marine organisms and their numerical abundance calculation using echo detection techniques remains a challenge for researchers. Different approaches using hydroacoustic techniques have been applied, alone or combined, to study marine and fresh water environments. An alternative approach to this problem is to use echoic statistics with confident geometric descriptors obtained from the echo-records processing. To accomplish this task it is necessary to have a development platform that allows reading echo-records from a particular echo-sounder, to detect aggregations or schools and then to calculate the various descriptors that will be use for species identification, in an automatic way. This article describes the application of digital processing algorithms of echo-records for automatic recognition of the ocean seabed, surface and marine organisms. These algorithms are implemented within the EcoPampa software, which is the first Argentinean open source system for the identification of marine species.

[2]  Rolf J. Korneliussen Advances in Bergen Echo Integrator , 1993 .

[3]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Ariel G. Cabreira,et al.  Artificial neural networks for fish-species identification , 2009 .

[5]  John K. Horne,et al.  Acoustic approaches to remote species identification: a review , 2000 .

[6]  Noël Diner,et al.  Correction on school geometry and density: approach based on acoustic image simulation , 2001 .

[7]  Jacques Masse,et al.  Acoustic detection of the spatial and temporal distribution of fish shoals in the Bay of Biscay , 1993 .

[8]  Philippe Blondel,et al.  The Handbook of Sidescan Sonar , 2009 .

[9]  Gerardo G. Acosta,et al.  Some Issues on the Design of a Low-Cost Autonomous Underwater Vehicle with an Intelligent Dynamic Mission Planner for Pipeline and Cable Tracking , 2009 .

[10]  R. Korneliussen,et al.  THE LARGE SCALE SURVEY SYSTEM-LSSS , 2006 .

[11]  José Antonio Cruz-Ledesma,et al.  Modelling, Design and Robust Control of a Remotely Operated Underwater Vehicle , 2014 .

[12]  Gerardo G. Acosta,et al.  Evaluation of an Efficient Approach for Target Tracking from Acoustic Imagery for the Perception System of an Autonomous Underwater Vehicle , 2014 .

[13]  Philippe Blondel,et al.  Handbook of seafloor sonar imagery , 1997 .

[14]  Kenneth G. Foote,et al.  Postprocessing system for echo sounder data , 1991 .

[15]  Paul G. Fernandes,et al.  Autonomous underwater vehicles: future platforms for fisheries acoustics , 2003 .

[16]  William K. Pratt,et al.  Digital Image Processing: PIKS Inside , 2001 .

[17]  E. Ona,et al.  Synthetic echograms generated from the relative frequency response , 2003 .

[18]  A. Aglen,et al.  How vertical fish distribution may affect survey results , 1999 .

[19]  John C. Russ,et al.  The Image Processing Handbook , 2016, Microscopy and Microanalysis.

[20]  J. Coetzee Use of a shoal analysis and patch estimation system (SHAPES) to characterise sardine schools , 2000 .