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Image dataset for benchmarking automated fish detection and classification algorithms.

Marco Francescangeli1, Simone Marini2,3, Enoc Martínez4

  • 1Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. marco.francescangeli@upc.edu.

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Summary
This summary is machine-generated.

Marine observatories provide continuous data for ecosystem monitoring. Automated analysis of fish images from the OBSEA observatory aids in developing AI for species identification, crucial for understanding marine environments.

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Area of Science:

  • Marine Ecology
  • Oceanography
  • Computer Science

Background:

  • Multiparametric video-cabled marine observatories are essential for real-time remote monitoring of marine ecosystems.
  • Continuous, high-frequency image data from these platforms necessitate automated methods for biological time-series extraction.

Purpose of the Study:

  • To generate coastal fish time series using data from the OBSEA (Oceanic Buoy System for Environmental Acoustic monitoring) observatory.
  • To create a comprehensive dataset of tagged fish and associated environmental conditions for AI development.

Main Methods:

  • The OBSEA observatory collected continuous 24-h imagery from 2013-2014 at a depth of 20 meters.
  • Image analysis involved tagging 69,917 fish across 30 taxa.
  • Meteorological and oceanographic data were collected and quality-controlled to correlate with image quality.

Main Results:

  • A substantial dataset of 69,917 tagged fish from 30 taxa was generated.
  • Environmental data provided context for observed fish populations.
  • The study successfully produced continuous coastal fish time series.

Conclusions:

  • The tagged fish dataset is valuable for developing Artificial Intelligence (AI) algorithms for automated fish identification and classification.
  • This approach enables efficient analysis of extensive time-lapse image sets from marine observatories.
  • Automated analysis is key to extracting meaningful biological insights from large-scale marine monitoring data.