Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection

During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In part...

Descripción completa

Detalles Bibliográficos
Autores: Martín-Abadal, Miguel, Ruiz-Frau, Ana, Hinz, Hilmar, González-Cid, Yolanda
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/223605
Acceso en línea:http://hdl.handle.net/10261/223605
Access Level:acceso abierto
Palabra clave:Deep learning
Object detection
Jellyfish quantification
Jellyfish monitoring
id ES_bf255fc94801bd82a45f1f1a5fc8e165
oai_identifier_str oai:digital.csic.es:10261/223605
network_acronym_str ES
network_name_str España
repository_id_str
spelling Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object DetectionMartín-Abadal, MiguelRuiz-Frau, AnaHinz, HilmarGonzález-Cid, YolandaDeep learningObject detectionJellyfish quantificationJellyfish monitoringDuring the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans.Miguel Martin-Abadal was supported by Ministry of Economy and Competitiveness (AEI,FEDER,UE), under contract DPI2017-86372-C3-3-R. Ana Ruiz-Frau was supported by a Marie-Sklodowska-Curie Individual Fellowship (JellyPacts project number 655475). Hilmar Hinz was supported through a Ramón y Cajal Fellowship financed by the Ministerio de Economía y Competitividad de España and the Conselleria d’Educació, Cultura i Universitats Comunidad Autónoma de las Islas Baleares (RyC 2013 14729). Yolanda Gonzalez-Cid was supported by Ministry of Economy and Competitiveness (AEI,FEDER,UE), under contracts TIN2017-85572-P and DPI2017-86372-C3-1-R.Peer reviewedMultidisciplinary Digital Publishing InstituteMinisterio de Economía y Competitividad (España)Ministerio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)Govern de les Illes BalearsEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202020info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/223605reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86372-C3-3-RDPI2017-86372-C3-3-R/AEI/10.13039/501100011033info:eu-repo/grantAgreement/EC/H2020/655475info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RYC-2013-14729info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TIN2017-85572-PTIN2017-85572-P/AEI/10.13039/501100011033info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86372-C3-1-RDPI2017-86372-C3-1-R/AEI/10.13039/501100011033https://doi.org/10.3390/s20061708Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2236052026-05-22T06:33:51Z
dc.title.none.fl_str_mv Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
title Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
spellingShingle Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
Martín-Abadal, Miguel
Deep learning
Object detection
Jellyfish quantification
Jellyfish monitoring
title_short Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
title_full Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
title_fullStr Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
title_full_unstemmed Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
title_sort Jellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
dc.creator.none.fl_str_mv Martín-Abadal, Miguel
Ruiz-Frau, Ana
Hinz, Hilmar
González-Cid, Yolanda
author Martín-Abadal, Miguel
author_facet Martín-Abadal, Miguel
Ruiz-Frau, Ana
Hinz, Hilmar
González-Cid, Yolanda
author_role author
author2 Ruiz-Frau, Ana
Hinz, Hilmar
González-Cid, Yolanda
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
Govern de les Illes Balears
European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Deep learning
Object detection
Jellyfish quantification
Jellyfish monitoring
topic Deep learning
Object detection
Jellyfish quantification
Jellyfish monitoring
description During the past decades, the composition and distribution of marine species have changed due to multiple anthropogenic pressures. Monitoring these changes in a cost-effective manner is of high relevance to assess the environmental status and evaluate the effectiveness of management measures. In particular, recent studies point to a rise of jellyfish populations on a global scale, negatively affecting diverse marine sectors like commercial fishing or the tourism industry. Past monitoring efforts using underwater video observations tended to be time-consuming and costly due to human-based data processing. In this paper, we present Jellytoring, a system to automatically detect and quantify different species of jellyfish based on a deep object detection neural network, allowing us to automatically record jellyfish presence during long periods of time. Jellytoring demonstrates outstanding performance on the jellyfish detection task, reaching an F1 score of 95.2%; and also on the jellyfish quantification task, as it correctly quantifies the number and class of jellyfish on a real-time processed video sequence up to a 93.8% of its duration. The results of this study are encouraging and provide the means towards a efficient way to monitor jellyfish, which can be used for the development of a jellyfish early-warning system, providing highly valuable information for marine biologists and contributing to the reduction of jellyfish impacts on humans.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/223605
url http://hdl.handle.net/10261/223605
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86372-C3-3-R
DPI2017-86372-C3-3-R/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/EC/H2020/655475
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RYC-2013-14729
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TIN2017-85572-P
TIN2017-85572-P/AEI/10.13039/501100011033
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86372-C3-1-R
DPI2017-86372-C3-1-R/AEI/10.13039/501100011033
https://doi.org/10.3390/s20061708
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869418344942141440
score 15,812429

Similares en LA Referencia