Image dataset for benchmarking automated fish detection and classification algorithms.

Journal: Scientific data
Published Date:

Abstract

Multiparametric video-cabled marine observatories are becoming strategic to monitor remotely and in real-time the marine ecosystem. Those platforms can achieve continuous, high-frequency and long-lasting image data sets that require automation in order to extract biological time series. The OBSEA, located at 4 km from Vilanova i la Geltrú at 20 m depth, was used to produce coastal fish time series continuously over the 24-h during 2013-2014. The image content of the photos was extracted via tagging, resulting in 69917 fish tags of 30 taxa identified. We also provided a meteorological and oceanographic dataset filtered by a quality control procedure to define real-world conditions affecting image quality. The tagged fish dataset can be of great importance to develop Artificial Intelligence routines for the automated identification and classification of fishes in extensive time-lapse image sets.

Authors

  • Marco Francescangeli
    Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. marco.francescangeli@upc.edu.
  • Simone Marini
    Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 1, 27100, Pavia, Italy. simone.marini@unipv.it.
  • Enoc Martínez
    European Multidisciplinary Seafloor and Water Column Observatory, Rome, Italy. enoc.martinez@upc.edu.
  • Joaquín Del Río
    Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. joaquin.del.rio@upc.edu.
  • Daniel M Toma
    Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. daniel.mihai.toma@upc.edu.
  • Marc Nogueras
    Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. marc.nogueras@upc.edu.
  • Jacopo Aguzzi
    Instituto de Cièncias del Mar (ICM-CSIC), E-08003 Barcelona, Spain.