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Automated detection of koalas using low-level aerial surveillance and machine learning
Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using publi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397288/ https://www.ncbi.nlm.nih.gov/pubmed/30824795 http://dx.doi.org/10.1038/s41598-019-39917-5 |
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author | Corcoran, Evangeline Denman, Simon Hanger, Jon Wilson, Bree Hamilton, Grant |
author_facet | Corcoran, Evangeline Denman, Simon Hanger, Jon Wilson, Bree Hamilton, Grant |
author_sort | Corcoran, Evangeline |
collection | PubMed |
description | Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using published object detection algorithms to detect their heat signatures in RPAS-derived thermal imaging. As an initial case study we used this new approach to detect koalas (Phascolarctus cinereus), and validated the approach using ground surveys of tracked radio-collared koalas in Petrie, Queensland. The automated method yielded a higher probability of detection (68–100%), higher precision (43–71%), lower root mean square error (RMSE), and lower mean absolute error (MAE) than manual assessment of the RPAS-derived thermal imagery in a comparable amount of time. This new approach allows for more reliable, less invasive detection of koalas in their natural habitat. This new detection methodology has great potential to inform and improve management decisions for threatened species, and other difficult to survey species. |
format | Online Article Text |
id | pubmed-6397288 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63972882019-03-05 Automated detection of koalas using low-level aerial surveillance and machine learning Corcoran, Evangeline Denman, Simon Hanger, Jon Wilson, Bree Hamilton, Grant Sci Rep Article Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using published object detection algorithms to detect their heat signatures in RPAS-derived thermal imaging. As an initial case study we used this new approach to detect koalas (Phascolarctus cinereus), and validated the approach using ground surveys of tracked radio-collared koalas in Petrie, Queensland. The automated method yielded a higher probability of detection (68–100%), higher precision (43–71%), lower root mean square error (RMSE), and lower mean absolute error (MAE) than manual assessment of the RPAS-derived thermal imagery in a comparable amount of time. This new approach allows for more reliable, less invasive detection of koalas in their natural habitat. This new detection methodology has great potential to inform and improve management decisions for threatened species, and other difficult to survey species. Nature Publishing Group UK 2019-03-01 /pmc/articles/PMC6397288/ /pubmed/30824795 http://dx.doi.org/10.1038/s41598-019-39917-5 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Corcoran, Evangeline Denman, Simon Hanger, Jon Wilson, Bree Hamilton, Grant Automated detection of koalas using low-level aerial surveillance and machine learning |
title | Automated detection of koalas using low-level aerial surveillance and machine learning |
title_full | Automated detection of koalas using low-level aerial surveillance and machine learning |
title_fullStr | Automated detection of koalas using low-level aerial surveillance and machine learning |
title_full_unstemmed | Automated detection of koalas using low-level aerial surveillance and machine learning |
title_short | Automated detection of koalas using low-level aerial surveillance and machine learning |
title_sort | automated detection of koalas using low-level aerial surveillance and machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397288/ https://www.ncbi.nlm.nih.gov/pubmed/30824795 http://dx.doi.org/10.1038/s41598-019-39917-5 |
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