FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales
Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetac...
Published in: | Scientific Reports |
---|---|
Main Authors: | , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/19533 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-195333 https://doi.org/10.1038/s41598-021-02506-6 https://opus4.kobv.de/opus4-fau/files/19533/s41598-021-02506-6.pdf |
id |
ftuniverlangen:oai:ub.uni-erlangen.de-opus:19533 |
---|---|
record_format |
openpolar |
spelling |
ftuniverlangen:oai:ub.uni-erlangen.de-opus:19533 2023-05-15T17:03:28+02:00 FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales Bergler, Christian Gebhard, Alexander Towers, Jared R. Butyrev, Leonid Sutton, Gary J. Shaw, Tasli J. H. Maier, Andreas Nöth, Elmar 2021 application/pdf https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/19533 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-195333 https://doi.org/10.1038/s41598-021-02506-6 https://opus4.kobv.de/opus4-fau/files/19533/s41598-021-02506-6.pdf eng eng https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/19533 urn:nbn:de:bvb:29-opus4-195333 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-195333 https://doi.org/10.1038/s41598-021-02506-6 https://opus4.kobv.de/opus4-fau/files/19533/s41598-021-02506-6.pdf https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess CC-BY ddc:000 article doc-type:article 2021 ftuniverlangen https://doi.org/10.1038/s41598-021-02506-6 2022-07-28T20:40:32Z Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011–2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg’s killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available. Article in Journal/Newspaper Killer Whale Killer whale OPUS FAU - Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg Scientific Reports 11 1 |
institution |
Open Polar |
collection |
OPUS FAU - Online publication system of Friedrich-Alexander-Universität Erlangen-Nürnberg |
op_collection_id |
ftuniverlangen |
language |
English |
topic |
ddc:000 |
spellingShingle |
ddc:000 Bergler, Christian Gebhard, Alexander Towers, Jared R. Butyrev, Leonid Sutton, Gary J. Shaw, Tasli J. H. Maier, Andreas Nöth, Elmar FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales |
topic_facet |
ddc:000 |
description |
Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg’s killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011–2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg’s killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available. |
format |
Article in Journal/Newspaper |
author |
Bergler, Christian Gebhard, Alexander Towers, Jared R. Butyrev, Leonid Sutton, Gary J. Shaw, Tasli J. H. Maier, Andreas Nöth, Elmar |
author_facet |
Bergler, Christian Gebhard, Alexander Towers, Jared R. Butyrev, Leonid Sutton, Gary J. Shaw, Tasli J. H. Maier, Andreas Nöth, Elmar |
author_sort |
Bergler, Christian |
title |
FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales |
title_short |
FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales |
title_full |
FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales |
title_fullStr |
FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales |
title_full_unstemmed |
FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales |
title_sort |
fin-print a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales |
publishDate |
2021 |
url |
https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/19533 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-195333 https://doi.org/10.1038/s41598-021-02506-6 https://opus4.kobv.de/opus4-fau/files/19533/s41598-021-02506-6.pdf |
genre |
Killer Whale Killer whale |
genre_facet |
Killer Whale Killer whale |
op_relation |
https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/19533 urn:nbn:de:bvb:29-opus4-195333 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-195333 https://doi.org/10.1038/s41598-021-02506-6 https://opus4.kobv.de/opus4-fau/files/19533/s41598-021-02506-6.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-021-02506-6 |
container_title |
Scientific Reports |
container_volume |
11 |
container_issue |
1 |
_version_ |
1766057334994370560 |