Standard
Automated Marking of Underwater Animals Using a Cascade of Neural Networks. / Iakushkin, Oleg; Pavlova, Ekaterina; Pen, Evgeniy; Frikh-Khar, Anna; Terekhina, Yana; Bulanova, Anna; Shabalin, Nikolay; Sedova, Olga.
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT VIII: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. ed. / O Gervasi; B Murgante; S Misra; C Garau; Blecic; D Taniar; BO Apduhan; AMAC Rocha; E Tarantino; CM Torre. Springer Nature, 2021. p. 460-470 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12956 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Iakushkin, O, Pavlova, E, Pen, E, Frikh-Khar, A, Terekhina, Y, Bulanova, A, Shabalin, N
& Sedova, O 2021,
Automated Marking of Underwater Animals Using a Cascade of Neural Networks. in O Gervasi, B Murgante, S Misra, C Garau, Blecic, D Taniar, BO Apduhan, AMAC Rocha, E Tarantino & CM Torre (eds),
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT VIII: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12956 LNCS, Springer Nature, pp. 460-470, 21st International Conference on Computational Science and Its Applications, ICCSA 2021, Virtual, Online, Italy,
13/09/21.
https://doi.org/10.1007/978-3-030-87010-2_34
APA
Iakushkin, O., Pavlova, E., Pen, E., Frikh-Khar, A., Terekhina, Y., Bulanova, A., Shabalin, N.
, & Sedova, O. (2021).
Automated Marking of Underwater Animals Using a Cascade of Neural Networks. In O. Gervasi, B. Murgante, S. Misra, C. Garau, Blecic, D. Taniar, BO. Apduhan, AMAC. Rocha, E. Tarantino, & CM. Torre (Eds.),
COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT VIII: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII (pp. 460-470). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12956 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-030-87010-2_34
Vancouver
Iakushkin O, Pavlova E, Pen E, Frikh-Khar A, Terekhina Y, Bulanova A et al.
Automated Marking of Underwater Animals Using a Cascade of Neural Networks. In Gervasi O, Murgante B, Misra S, Garau C, Blecic, Taniar D, Apduhan BO, Rocha AMAC, Tarantino E, Torre CM, editors, COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT VIII: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. Springer Nature. 2021. p. 460-470. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-87010-2_34
Author
Iakushkin, Oleg ; Pavlova, Ekaterina ; Pen, Evgeniy ; Frikh-Khar, Anna ; Terekhina, Yana ; Bulanova, Anna ; Shabalin, Nikolay
; Sedova, Olga. /
Automated Marking of Underwater Animals Using a Cascade of Neural Networks. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT VIII: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII. editor / O Gervasi ; B Murgante ; S Misra ; C Garau ; Blecic ; D Taniar ; BO Apduhan ; AMAC Rocha ; E Tarantino ; CM Torre. Springer Nature, 2021. pp. 460-470 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{05db5dc8f702455387894fd55378ddf6,
title = "Automated Marking of Underwater Animals Using a Cascade of Neural Networks",
abstract = "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.",
keywords = "Few-shot learning, Neural networks, Segmentation, Video analysis",
author = "Oleg Iakushkin and Ekaterina Pavlova and Evgeniy Pen and Anna Frikh-Khar and Yana Terekhina and Anna Bulanova and Nikolay Shabalin and Olga Sedova",
note = "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.; 21st International Conference on Computational Science and Its Applications, ICCSA 2021, ICCSA 2021 ; Conference date: 13-09-2021 Through 16-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87010-2_34",
language = "English",
isbn = "9783030870096",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "460--470",
editor = "O Gervasi and B Murgante and S Misra and C Garau and Blecic and D Taniar and BO Apduhan and AMAC Rocha and E Tarantino and CM Torre",
booktitle = "COMPUTATIONAL SCIENCE AND ITS APPLICATIONS, ICCSA 2021, PT VIII",
address = "Germany",
}
RIS
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 -
