Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.

Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Maria Sokolova, Fletcher Thompson, Patrizio Mariani, Ludvig Ahm Krag
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5ada4e32d6cc41dface0860224d446d0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5ada4e32d6cc41dface0860224d446d0
record_format	dspace
spelling	oai:doaj.org-article:5ada4e32d6cc41dface0860224d446d02021-12-02T20:10:35ZTowards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.1932-620310.1371/journal.pone.0252824https://doaj.org/article/5ada4e32d6cc41dface0860224d446d02021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252824https://doaj.org/toc/1932-6203Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.Maria SokolovaFletcher ThompsonPatrizio MarianiLudvig Ahm KragPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252824 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maria Sokolova Fletcher Thompson Patrizio Mariani Ludvig Ahm Krag Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.
description	Underwater video monitoring systems are being widely used in fisheries to investigate fish behavior in relation to fishing gear and fishing gear performance during fishing. Such systems can be useful to evaluate the catch composition as well. In demersal trawl fisheries, however, their applicability can be challenged by low light conditions, mobilized sediment and scattering in murky waters. In this study, we introduce a novel observation system (called NepCon) which aims at reducing current limitations by combining an optimized image acquisition setup and tailored image analyses software. The NepCon system includes a high-contrast background to enhance the visibility of the target objects, a compact camera and an artificial light source. The image analysis software includes a machine learning algorithm which is evaluated here to test automatic detection and count of Norway lobster (Nephrops norvegicus). NepCon is specifically designed for applications in demersal trawls and this first phase aims at increasing the accuracy of N. norvegicus detection at the data acquisition level. To find the best contrasting background for the purpose we compared the output of four image segmentation methods applied to static images of N. norvegicus fixed in front of four test background colors. The background color with the best performance was then used to evaluate computer vision and deep learning approaches for automatic detection, tracking and counting of N. norvegicus in the videos. In this initial phase we tested the system in an experimental setting to understand the feasibility of the system for future implementation in real demersal fishing conditions. The N. norvegicus directed trawl fishery typically has no assistance from underwater observation technology and therefore are largely conducted blindly. The demonstrated perception system achieves 76% accuracy (F-score) in automatic detection and count of N. norvegicus, which provides a significant elevation of the current benchmark.
format	article
author	Maria Sokolova Fletcher Thompson Patrizio Mariani Ludvig Ahm Krag
author_facet	Maria Sokolova Fletcher Thompson Patrizio Mariani Ludvig Ahm Krag
author_sort	Maria Sokolova
title	Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.
title_short	Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.
title_full	Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.
title_fullStr	Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.
title_full_unstemmed	Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.
title_sort	towards sustainable demersal fisheries: nepcon image acquisition system for automatic nephrops norvegicus detection.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/5ada4e32d6cc41dface0860224d446d0
work_keys_str_mv	AT mariasokolova towardssustainabledemersalfisheriesnepconimageacquisitionsystemforautomaticnephropsnorvegicusdetection AT fletcherthompson towardssustainabledemersalfisheriesnepconimageacquisitionsystemforautomaticnephropsnorvegicusdetection AT patriziomariani towardssustainabledemersalfisheriesnepconimageacquisitionsystemforautomaticnephropsnorvegicusdetection AT ludvigahmkrag towardssustainabledemersalfisheriesnepconimageacquisitionsystemforautomaticnephropsnorvegicusdetection
_version_	1718375001085706240

Towards sustainable demersal fisheries: NepCon image acquisition system for automatic Nephrops norvegicus detection.

Ejemplares similares