Wild salmon enumeration and monitoring using deep learning empowered detection and tracking
Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning...
Published in: | Frontiers in Marine Science |
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2023
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Online Access: | https://doi.org/10.3389/fmars.2023.1200408 https://doaj.org/article/7b9c5ab60640430a8c870d8f81462ef7 |
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ftdoajarticles:oai:doaj.org/article:7b9c5ab60640430a8c870d8f81462ef7 2023-10-09T21:51:35+02:00 Wild salmon enumeration and monitoring using deep learning empowered detection and tracking William I. Atlas Sami Ma Yi Ching Chou Katrina Connors Daniel Scurfield Brandon Nam Xiaoqiang Ma Mark Cleveland Janvier Doire Jonathan W. Moore Ryan Shea Jiangchuan Liu 2023-09-01T00:00:00Z https://doi.org/10.3389/fmars.2023.1200408 https://doaj.org/article/7b9c5ab60640430a8c870d8f81462ef7 EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/fmars.2023.1200408/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2023.1200408 https://doaj.org/article/7b9c5ab60640430a8c870d8f81462ef7 Frontiers in Marine Science, Vol 10 (2023) computer vision deep learning fisheries management in-season fishery management indigenous science wild salmon Science Q General. Including nature conservation geographical distribution QH1-199.5 article 2023 ftdoajarticles https://doi.org/10.3389/fmars.2023.1200408 2023-09-24T00:37:17Z Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers’ salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. ... Article in Journal/Newspaper First Nations Directory of Open Access Journals: DOAJ Articles British Columbia ENVELOPE(-125.003,-125.003,54.000,54.000) Canada Kitwanga ENVELOPE(-128.070,-128.070,55.100,55.100) Pacific Sockeye ENVELOPE(-130.143,-130.143,54.160,54.160) Frontiers in Marine Science 10 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
computer vision deep learning fisheries management in-season fishery management indigenous science wild salmon Science Q General. Including nature conservation geographical distribution QH1-199.5 |
spellingShingle |
computer vision deep learning fisheries management in-season fishery management indigenous science wild salmon Science Q General. Including nature conservation geographical distribution QH1-199.5 William I. Atlas Sami Ma Yi Ching Chou Katrina Connors Daniel Scurfield Brandon Nam Xiaoqiang Ma Mark Cleveland Janvier Doire Jonathan W. Moore Ryan Shea Jiangchuan Liu Wild salmon enumeration and monitoring using deep learning empowered detection and tracking |
topic_facet |
computer vision deep learning fisheries management in-season fishery management indigenous science wild salmon Science Q General. Including nature conservation geographical distribution QH1-199.5 |
description |
Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers’ salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. ... |
format |
Article in Journal/Newspaper |
author |
William I. Atlas Sami Ma Yi Ching Chou Katrina Connors Daniel Scurfield Brandon Nam Xiaoqiang Ma Mark Cleveland Janvier Doire Jonathan W. Moore Ryan Shea Jiangchuan Liu |
author_facet |
William I. Atlas Sami Ma Yi Ching Chou Katrina Connors Daniel Scurfield Brandon Nam Xiaoqiang Ma Mark Cleveland Janvier Doire Jonathan W. Moore Ryan Shea Jiangchuan Liu |
author_sort |
William I. Atlas |
title |
Wild salmon enumeration and monitoring using deep learning empowered detection and tracking |
title_short |
Wild salmon enumeration and monitoring using deep learning empowered detection and tracking |
title_full |
Wild salmon enumeration and monitoring using deep learning empowered detection and tracking |
title_fullStr |
Wild salmon enumeration and monitoring using deep learning empowered detection and tracking |
title_full_unstemmed |
Wild salmon enumeration and monitoring using deep learning empowered detection and tracking |
title_sort |
wild salmon enumeration and monitoring using deep learning empowered detection and tracking |
publisher |
Frontiers Media S.A. |
publishDate |
2023 |
url |
https://doi.org/10.3389/fmars.2023.1200408 https://doaj.org/article/7b9c5ab60640430a8c870d8f81462ef7 |
long_lat |
ENVELOPE(-125.003,-125.003,54.000,54.000) ENVELOPE(-128.070,-128.070,55.100,55.100) ENVELOPE(-130.143,-130.143,54.160,54.160) |
geographic |
British Columbia Canada Kitwanga Pacific Sockeye |
geographic_facet |
British Columbia Canada Kitwanga Pacific Sockeye |
genre |
First Nations |
genre_facet |
First Nations |
op_source |
Frontiers in Marine Science, Vol 10 (2023) |
op_relation |
https://www.frontiersin.org/articles/10.3389/fmars.2023.1200408/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2023.1200408 https://doaj.org/article/7b9c5ab60640430a8c870d8f81462ef7 |
op_doi |
https://doi.org/10.3389/fmars.2023.1200408 |
container_title |
Frontiers in Marine Science |
container_volume |
10 |
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1779314712387780608 |