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Image dataset for benchmarking automated fish detection and classification algorithms.
Francescangeli, Marco; Marini, Simone; Martínez, Enoc; Del Río, Joaquín; Toma, Daniel M; Nogueras, Marc; Aguzzi, Jacopo.
Affiliation
  • Francescangeli M; Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. marco.francescangeli@upc.edu.
  • Marini S; Institute of Marine Sciences, National Research Council of Italy, La Spezia, Italy. simone.marini@sp.ismar.cnr.it.
  • Martínez E; Stazione Zoologica Anton Dohrn (SZN), Naples, 80127, Italy. simone.marini@sp.ismar.cnr.it.
  • Del Río J; European Multidisciplinary Seafloor and Water Column Observatory, Rome, Italy. enoc.martinez@upc.edu.
  • Toma DM; Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. joaquin.del.rio@upc.edu.
  • Nogueras M; Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. daniel.mihai.toma@upc.edu.
  • Aguzzi J; Electronics Department, Polytechnic University of Catalonia (UPC), Vilanova i la Geltrú, Barcelona, 08800, Spain. marc.nogueras@upc.edu.
Sci Data ; 10(1): 5, 2023 01 03.
Article in En | MEDLINE | ID: mdl-36596792
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Ecosystem / Fishes Type of study: Diagnostic_studies Limits: Animals Language: En Journal: Sci Data Year: 2023 Document type: Article Affiliation country: Spain Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Ecosystem / Fishes Type of study: Diagnostic_studies Limits: Animals Language: En Journal: Sci Data Year: 2023 Document type: Article Affiliation country: Spain Country of publication: United kingdom