Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System
Estimating seabird and pinniped abundance is central to wildlife management and ecosystem monitoring in Antarctica. Unoccupied aerial systems (UAS) can collect images to support monitoring, but manual image analysis is often impractical. Automating target detection using deep learning techniques may...
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ftdoajarticles:oai:doaj.org/article:d29ec75c33e74e18b85e4dec66cf01a4 2023-05-15T13:34:52+02:00 Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System Jefferson T. Hinke Louise M. Giuseffi Victoria R. Hermanson Samuel M. Woodman Douglas J. Krause 2022-09-01T00:00:00Z https://doi.org/10.3390/drones6090255 https://doaj.org/article/d29ec75c33e74e18b85e4dec66cf01a4 EN eng MDPI AG https://www.mdpi.com/2504-446X/6/9/255 https://doaj.org/toc/2504-446X doi:10.3390/drones6090255 2504-446X https://doaj.org/article/d29ec75c33e74e18b85e4dec66cf01a4 Drones, Vol 6, Iss 255, p 255 (2022) automated detection Antarctica drone census image analysis Motor vehicles. Aeronautics. Astronautics TL1-4050 article 2022 ftdoajarticles https://doi.org/10.3390/drones6090255 2022-12-30T19:58:21Z Estimating seabird and pinniped abundance is central to wildlife management and ecosystem monitoring in Antarctica. Unoccupied aerial systems (UAS) can collect images to support monitoring, but manual image analysis is often impractical. Automating target detection using deep learning techniques may improve data acquisition, but different image sensors may affect target detectability and model performance. We compared the performance of automated detection models based on infrared (IR) or color (RGB) images and tested whether IR images, or training data that included annotations of non-target features, improved model performance. For this assessment, we collected paired IR and RGB images of nesting penguins (Pygoscelis spp.) and aggregations of Antarctic fur seals ( Arctocephalus gazella ) with a small UAS at Cape Shirreff, Livingston Island (60.79 °W, 62.46 °S). We trained seven independent classification models using the Video and Image Analytics for Marine Environments (VIAME) software and created an open-access R tool, vvipr, to standardize the assessment of VIAME-based model performance. We found that the IR images and the addition of non-target annotations had no clear benefits for model performance given the available data. Nonetheless, the generally high performance of the penguin models provided encouraging results for further improving automated image analysis from UAS surveys. Article in Journal/Newspaper Antarc* Antarctic Antarctic Fur Seals Antarctica Arctocephalus gazella Livingston Island Directory of Open Access Journals: DOAJ Articles Antarctic Livingston Island ENVELOPE(-60.500,-60.500,-62.600,-62.600) Shirreff ENVELOPE(-60.792,-60.792,-62.459,-62.459) Cape Shirreff ENVELOPE(-60.800,-60.800,-62.417,-62.417) Drones 6 9 255 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
automated detection Antarctica drone census image analysis Motor vehicles. Aeronautics. Astronautics TL1-4050 |
spellingShingle |
automated detection Antarctica drone census image analysis Motor vehicles. Aeronautics. Astronautics TL1-4050 Jefferson T. Hinke Louise M. Giuseffi Victoria R. Hermanson Samuel M. Woodman Douglas J. Krause Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System |
topic_facet |
automated detection Antarctica drone census image analysis Motor vehicles. Aeronautics. Astronautics TL1-4050 |
description |
Estimating seabird and pinniped abundance is central to wildlife management and ecosystem monitoring in Antarctica. Unoccupied aerial systems (UAS) can collect images to support monitoring, but manual image analysis is often impractical. Automating target detection using deep learning techniques may improve data acquisition, but different image sensors may affect target detectability and model performance. We compared the performance of automated detection models based on infrared (IR) or color (RGB) images and tested whether IR images, or training data that included annotations of non-target features, improved model performance. For this assessment, we collected paired IR and RGB images of nesting penguins (Pygoscelis spp.) and aggregations of Antarctic fur seals ( Arctocephalus gazella ) with a small UAS at Cape Shirreff, Livingston Island (60.79 °W, 62.46 °S). We trained seven independent classification models using the Video and Image Analytics for Marine Environments (VIAME) software and created an open-access R tool, vvipr, to standardize the assessment of VIAME-based model performance. We found that the IR images and the addition of non-target annotations had no clear benefits for model performance given the available data. Nonetheless, the generally high performance of the penguin models provided encouraging results for further improving automated image analysis from UAS surveys. |
format |
Article in Journal/Newspaper |
author |
Jefferson T. Hinke Louise M. Giuseffi Victoria R. Hermanson Samuel M. Woodman Douglas J. Krause |
author_facet |
Jefferson T. Hinke Louise M. Giuseffi Victoria R. Hermanson Samuel M. Woodman Douglas J. Krause |
author_sort |
Jefferson T. Hinke |
title |
Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System |
title_short |
Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System |
title_full |
Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System |
title_fullStr |
Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System |
title_full_unstemmed |
Evaluating Thermal and Color Sensors for Automating Detection of Penguins and Pinnipeds in Images Collected with an Unoccupied Aerial System |
title_sort |
evaluating thermal and color sensors for automating detection of penguins and pinnipeds in images collected with an unoccupied aerial system |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/drones6090255 https://doaj.org/article/d29ec75c33e74e18b85e4dec66cf01a4 |
long_lat |
ENVELOPE(-60.500,-60.500,-62.600,-62.600) ENVELOPE(-60.792,-60.792,-62.459,-62.459) ENVELOPE(-60.800,-60.800,-62.417,-62.417) |
geographic |
Antarctic Livingston Island Shirreff Cape Shirreff |
geographic_facet |
Antarctic Livingston Island Shirreff Cape Shirreff |
genre |
Antarc* Antarctic Antarctic Fur Seals Antarctica Arctocephalus gazella Livingston Island |
genre_facet |
Antarc* Antarctic Antarctic Fur Seals Antarctica Arctocephalus gazella Livingston Island |
op_source |
Drones, Vol 6, Iss 255, p 255 (2022) |
op_relation |
https://www.mdpi.com/2504-446X/6/9/255 https://doaj.org/toc/2504-446X doi:10.3390/drones6090255 2504-446X https://doaj.org/article/d29ec75c33e74e18b85e4dec66cf01a4 |
op_doi |
https://doi.org/10.3390/drones6090255 |
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Drones |
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6 |
container_issue |
9 |
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255 |
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