Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
Abstract Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high‐resolution data retrieval. Here, we e...
Published in: | Remote Sensing in Ecology and Conservation |
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ftdoajarticles:oai:doaj.org/article:aea561be33084f1683e8dffdb8efc620 2023-09-26T15:23:55+02:00 Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research Jonas Hentati‐Sundberg Agnes B. Olin Sheetal Reddy Per‐Arvid Berglund Erik Svensson Mareddy Reddy Siddharta Kasarareni Astrid A. Carlsen Matilda Hanes Shreyash Kad Olof Olsson 2023-08-01T00:00:00Z https://doi.org/10.1002/rse2.329 https://doaj.org/article/aea561be33084f1683e8dffdb8efc620 EN eng Wiley https://doi.org/10.1002/rse2.329 https://doaj.org/toc/2056-3485 2056-3485 doi:10.1002/rse2.329 https://doaj.org/article/aea561be33084f1683e8dffdb8efc620 Remote Sensing in Ecology and Conservation, Vol 9, Iss 4, Pp 568-581 (2023) Artificial intelligence machine learning monitoring object detection seabirds Technology T Ecology QH540-549.5 article 2023 ftdoajarticles https://doi.org/10.1002/rse2.329 2023-08-27T00:34:06Z Abstract Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high‐resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame‐by‐frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high‐resolution up‐to‐date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up‐to‐date support for conservation and ecosystem management. Article in Journal/Newspaper Uria aalge uria Directory of Open Access Journals: DOAJ Articles Remote Sensing in Ecology and Conservation 9 4 568 581 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Artificial intelligence machine learning monitoring object detection seabirds Technology T Ecology QH540-549.5 |
spellingShingle |
Artificial intelligence machine learning monitoring object detection seabirds Technology T Ecology QH540-549.5 Jonas Hentati‐Sundberg Agnes B. Olin Sheetal Reddy Per‐Arvid Berglund Erik Svensson Mareddy Reddy Siddharta Kasarareni Astrid A. Carlsen Matilda Hanes Shreyash Kad Olof Olsson Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research |
topic_facet |
Artificial intelligence machine learning monitoring object detection seabirds Technology T Ecology QH540-549.5 |
description |
Abstract Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high‐resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame‐by‐frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high‐resolution up‐to‐date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up‐to‐date support for conservation and ecosystem management. |
format |
Article in Journal/Newspaper |
author |
Jonas Hentati‐Sundberg Agnes B. Olin Sheetal Reddy Per‐Arvid Berglund Erik Svensson Mareddy Reddy Siddharta Kasarareni Astrid A. Carlsen Matilda Hanes Shreyash Kad Olof Olsson |
author_facet |
Jonas Hentati‐Sundberg Agnes B. Olin Sheetal Reddy Per‐Arvid Berglund Erik Svensson Mareddy Reddy Siddharta Kasarareni Astrid A. Carlsen Matilda Hanes Shreyash Kad Olof Olsson |
author_sort |
Jonas Hentati‐Sundberg |
title |
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research |
title_short |
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research |
title_full |
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research |
title_fullStr |
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research |
title_full_unstemmed |
Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research |
title_sort |
seabird surveillance: combining cctv and artificial intelligence for monitoring and research |
publisher |
Wiley |
publishDate |
2023 |
url |
https://doi.org/10.1002/rse2.329 https://doaj.org/article/aea561be33084f1683e8dffdb8efc620 |
genre |
Uria aalge uria |
genre_facet |
Uria aalge uria |
op_source |
Remote Sensing in Ecology and Conservation, Vol 9, Iss 4, Pp 568-581 (2023) |
op_relation |
https://doi.org/10.1002/rse2.329 https://doaj.org/toc/2056-3485 2056-3485 doi:10.1002/rse2.329 https://doaj.org/article/aea561be33084f1683e8dffdb8efc620 |
op_doi |
https://doi.org/10.1002/rse2.329 |
container_title |
Remote Sensing in Ecology and Conservation |
container_volume |
9 |
container_issue |
4 |
container_start_page |
568 |
op_container_end_page |
581 |
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1778150295951376384 |