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A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis
Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809566/ https://www.ncbi.nlm.nih.gov/pubmed/35108340 http://dx.doi.org/10.1371/journal.pone.0263377 |
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author | Bilodeau, Stephanie M. Schwartz, Austin W. H. Xu, Binfeng Paúl Pauca, V. Silman, Miles R. |
author_facet | Bilodeau, Stephanie M. Schwartz, Austin W. H. Xu, Binfeng Paúl Pauca, V. Silman, Miles R. |
author_sort | Bilodeau, Stephanie M. |
collection | PubMed |
description | Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques’ use by addressing fish movement and feeding behavior related to halos, a well-documented benthic pattern in shallow tropical reefscapes. Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months and collected a total of over 100,000 images in time-lapse mode (by 15 minutes) during daylight hours. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fishes, and diver surveys revealed that the camera images accurately represented local fish communities. The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras’ potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns. |
format | Online Article Text |
id | pubmed-8809566 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-88095662022-02-03 A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis Bilodeau, Stephanie M. Schwartz, Austin W. H. Xu, Binfeng Paúl Pauca, V. Silman, Miles R. PLoS One Research Article Understanding long-term trends in marine ecosystems requires accurate and repeatable counts of fishes and other aquatic organisms on spatial and temporal scales that are difficult or impossible to achieve with diver-based surveys. Long-term, spatially distributed cameras, like those used in terrestrial camera trapping, have not been successfully applied in marine systems due to limitations of the aquatic environment. Here, we develop methodology for a system of low-cost, long-term camera traps (Dispersed Environment Aquatic Cameras), deployable over large spatial scales in remote marine environments. We use machine learning to classify the large volume of images collected by the cameras. We present a case study of these combined techniques’ use by addressing fish movement and feeding behavior related to halos, a well-documented benthic pattern in shallow tropical reefscapes. Cameras proved able to function continuously underwater at deployed depths (up to 7 m, with later versions deployed to 40 m) with no maintenance or monitoring for over five months and collected a total of over 100,000 images in time-lapse mode (by 15 minutes) during daylight hours. Our ResNet-50-based deep learning model achieved 92.5% overall accuracy in sorting images with and without fishes, and diver surveys revealed that the camera images accurately represented local fish communities. The cameras and machine learning classification represent the first successful method for broad-scale underwater camera trap deployment, and our case study demonstrates the cameras’ potential for addressing questions of marine animal behavior, distributions, and large-scale spatial patterns. Public Library of Science 2022-02-02 /pmc/articles/PMC8809566/ /pubmed/35108340 http://dx.doi.org/10.1371/journal.pone.0263377 Text en © 2022 Bilodeau et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Bilodeau, Stephanie M. Schwartz, Austin W. H. Xu, Binfeng Paúl Pauca, V. Silman, Miles R. A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis |
title | A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis |
title_full | A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis |
title_fullStr | A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis |
title_full_unstemmed | A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis |
title_short | A low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis |
title_sort | low-cost, long-term underwater camera trap network coupled with deep residual learning image analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8809566/ https://www.ncbi.nlm.nih.gov/pubmed/35108340 http://dx.doi.org/10.1371/journal.pone.0263377 |
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