Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys
Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model p...
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ftpubmed:oai:pubmedcentral.nih.gov:9849265 2023-05-15T18:04:21+02:00 Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys Lenzi, Javier Barnas, Andrew F. ElSaid, Abdelrahman A. Desell, Travis Rockwell, Robert F. Ellis-Felege, Susan N. 2023-01-18 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849265/ https://doi.org/10.1038/s41598-023-28240-9 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849265/ http://dx.doi.org/10.1038/s41598-023-28240-9 © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . CC-BY Sci Rep Article Text 2023 ftpubmed https://doi.org/10.1038/s41598-023-28240-9 2023-01-22T02:10:54Z Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: “adult caribou”, “calf caribou”, and “ghost caribou” (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96–0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers’ annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities. Text Rangifer tarandus PubMed Central (PMC) Scientific Reports 13 1 |
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Article Lenzi, Javier Barnas, Andrew F. ElSaid, Abdelrahman A. Desell, Travis Rockwell, Robert F. Ellis-Felege, Susan N. Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
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Imagery from drones is becoming common in wildlife research and management, but processing data efficiently remains a challenge. We developed a methodology for training a convolutional neural network model on large-scale mosaic imagery to detect and count caribou (Rangifer tarandus), compare model performance with an experienced observer and a group of naïve observers, and discuss the use of aerial imagery and automated methods for large mammal surveys. Combining images taken at 75 m and 120 m above ground level, a faster region-based convolutional neural network (Faster-RCNN) model was trained in using annotated imagery with the labels: “adult caribou”, “calf caribou”, and “ghost caribou” (animals moving between images, producing blurring individuals during the photogrammetry processing). Accuracy, precision, and recall of the model were 80%, 90%, and 88%, respectively. Detections between the model and experienced observer were highly correlated (Pearson: 0.96–0.99, P value < 0.05). The model was generally more effective in detecting adults, calves, and ghosts than naïve observers at both altitudes. We also discuss the need to improve consistency of observers’ annotations if manual review will be used to train models accurately. Generalization of automated methods for large mammal detections will be necessary for large-scale studies with diverse platforms, airspace restrictions, and sensor capabilities. |
format |
Text |
author |
Lenzi, Javier Barnas, Andrew F. ElSaid, Abdelrahman A. Desell, Travis Rockwell, Robert F. Ellis-Felege, Susan N. |
author_facet |
Lenzi, Javier Barnas, Andrew F. ElSaid, Abdelrahman A. Desell, Travis Rockwell, Robert F. Ellis-Felege, Susan N. |
author_sort |
Lenzi, Javier |
title |
Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
title_short |
Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
title_full |
Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
title_fullStr |
Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
title_full_unstemmed |
Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
title_sort |
artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
publisher |
Nature Publishing Group UK |
publishDate |
2023 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849265/ https://doi.org/10.1038/s41598-023-28240-9 |
genre |
Rangifer tarandus |
genre_facet |
Rangifer tarandus |
op_source |
Sci Rep |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849265/ http://dx.doi.org/10.1038/s41598-023-28240-9 |
op_rights |
© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1038/s41598-023-28240-9 |
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Scientific Reports |
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