Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
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 th...
Published in: | Remote Sensing in Ecology and Conservation |
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Online Access: | https://doi.org/10.1002/rse2.329 https://research.chalmers.se/en/publication/535132 |
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ftchalmersuniv:oai:research.chalmers.se:535132 2023-07-16T04:01:12+02:00 Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research Hentati-Sundberg, Jonas Olin, Agnes B. Reddy, Sheetal Berglund, Per Arvid Svensson, Erik Reddy, Mareddy Kasarareni, Siddharta Carlsen, Astrid A. Hanes, Matilda Kad, Shreyash Olsson, Olof 2023 text https://doi.org/10.1002/rse2.329 https://research.chalmers.se/en/publication/535132 unknown http://dx.doi.org/10.1002/rse2.329 https://research.chalmers.se/en/publication/535132 Other Computer and Information Science Other Engineering and Technologies not elsewhere specified Computer Vision and Robotics (Autonomous Systems) machine learning monitoring object detection Artificial intelligence seabirds 2023 ftchalmersuniv https://doi.org/10.1002/rse2.329 2023-06-28T22:36:01Z 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. Other/Unknown Material Uria aalge uria Chalmers University of Technology: Chalmers research Remote Sensing in Ecology and Conservation |
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Chalmers University of Technology: Chalmers research |
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ftchalmersuniv |
language |
unknown |
topic |
Other Computer and Information Science Other Engineering and Technologies not elsewhere specified Computer Vision and Robotics (Autonomous Systems) machine learning monitoring object detection Artificial intelligence seabirds |
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Other Computer and Information Science Other Engineering and Technologies not elsewhere specified Computer Vision and Robotics (Autonomous Systems) machine learning monitoring object detection Artificial intelligence seabirds Hentati-Sundberg, Jonas Olin, Agnes B. Reddy, Sheetal Berglund, Per Arvid Svensson, Erik Reddy, Mareddy Kasarareni, Siddharta Carlsen, Astrid A. Hanes, Matilda Kad, Shreyash Olsson, Olof Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research |
topic_facet |
Other Computer and Information Science Other Engineering and Technologies not elsewhere specified Computer Vision and Robotics (Autonomous Systems) machine learning monitoring object detection Artificial intelligence seabirds |
description |
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. |
author |
Hentati-Sundberg, Jonas Olin, Agnes B. Reddy, Sheetal Berglund, Per Arvid Svensson, Erik Reddy, Mareddy Kasarareni, Siddharta Carlsen, Astrid A. Hanes, Matilda Kad, Shreyash Olsson, Olof |
author_facet |
Hentati-Sundberg, Jonas Olin, Agnes B. Reddy, Sheetal Berglund, Per Arvid Svensson, Erik Reddy, Mareddy Kasarareni, Siddharta Carlsen, Astrid A. Hanes, Matilda Kad, Shreyash Olsson, Olof |
author_sort |
Hentati-Sundberg, Jonas |
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 |
publishDate |
2023 |
url |
https://doi.org/10.1002/rse2.329 https://research.chalmers.se/en/publication/535132 |
genre |
Uria aalge uria |
genre_facet |
Uria aalge uria |
op_relation |
http://dx.doi.org/10.1002/rse2.329 https://research.chalmers.se/en/publication/535132 |
op_doi |
https://doi.org/10.1002/rse2.329 |
container_title |
Remote Sensing in Ecology and Conservation |
_version_ |
1771550781901635584 |