TY - GEN

T1 - Automated Marking of Underwater Animals Using a Cascade of Neural Networks

AU - Iakushkin, Oleg

AU - Pavlova, Ekaterina

AU - Pen, Evgeniy

AU - Frikh-Khar, Anna

AU - Terekhina, Yana

AU - Bulanova, Anna

AU - Shabalin, Nikolay

AU - Sedova, Olga

N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Few-shot learning

KW - Neural networks

KW - Segmentation

KW - Video analysis

UR - http://www.scopus.com/inward/record.url?scp=85115722253&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/71cf435c-9b76-30d9-a700-d4bd35cf85a5/

U2 - 10.1007/978-3-030-87010-2_34

DO - 10.1007/978-3-030-87010-2_34

M3 - Conference contribution

AN - SCOPUS:85115722253

SN - 9783030870096

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 460

EP - 470

BT - COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT VIII

A2 - Gervasi, O

A2 - Murgante, B

A2 - Misra, S

A2 - Garau, C

A2 - Blecic, null

A2 - Taniar, D

A2 - Apduhan, BO

A2 - Rocha, AMAC

A2 - Tarantino, E

A2 - Torre, CM

PB - Springer Nature

T2 - 21st International Conference on Computational Science and Its Applications, ICCSA 2021

Y2 - 13 September 2021 through 16 September 2021

ER -