Automated Marking of Underwater Animals Using a Cascade of Neural Networks

Oleg Iakushkin, Ekaterina Pavlova, Evgeniy Pen, Anna Frikh-Khar, Yana Terekhina, Anna Bulanova, Nikolay Shabalin, Olga Sedova

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this work, a multifactorial problem of analyzing the seabed state of plants and animals using photo and video materials is considered. Marine research to monitor benthic communities and automatic mapping of underwater landscapes make it possible to qualitatively assess the state of biomes. The task includes several components: preparation of a methodology for data analysis, their aggregation, analysis, presentation of results. In this work, we focused on methods for automating detection and data presentation. For deep-sea research, which involves the detection, counting and segmentation of plants and animals, it is difficult to use traditional computer vision techniques. Thanks to modern automated monitoring technologies, the speed and quality of research can be increased several times while reducing the required human resources using machine learning and interactive visualization methods. The proposed approach significantly improves the quality of the segmentation of objects underwater. The algorithm includes three main stages: correction of image distortions underwater, image segmentation, selection of individual objects. Combining neural networks that successfully solve each of the tasks separately into a cascade of neural networks is the optimal method for solving the problem of segmentation of aquaculture and animals. Using the results obtained, it is possible to facilitate the control of the ecological state in the world, to automate the task of monitoring underwater populations.

Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2021
Subtitle of host publication21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII
EditorsOsvaldo Gervasi, et al.
PublisherSpringer Nature
ISBN (Print)9783030870096
StateE-pub ahead of print - 10 Sep 2021
Event21st International Conference on Computational Science and Its Applications, ICCSA 2021 - Virtual, Online
Duration: 13 Sep 202116 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12956 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Computational Science and Its Applications, ICCSA 2021
CityVirtual, Online

Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


  • Few-shot learning
  • Neural networks
  • Segmentation
  • Video analysis


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