Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys
Abstract 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), compar...
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ftdoajarticles:oai:doaj.org/article:ed21980fda49496a81a363156555dc22 2023-05-15T18:04:21+02:00 Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys Javier Lenzi Andrew F. Barnas Abdelrahman A. ElSaid Travis Desell Robert F. Rockwell Susan N. Ellis-Felege 2023-01-01T00:00:00Z https://doi.org/10.1038/s41598-023-28240-9 https://doaj.org/article/ed21980fda49496a81a363156555dc22 EN eng Nature Portfolio https://doi.org/10.1038/s41598-023-28240-9 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-023-28240-9 2045-2322 https://doaj.org/article/ed21980fda49496a81a363156555dc22 Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023) Medicine R Science Q article 2023 ftdoajarticles https://doi.org/10.1038/s41598-023-28240-9 2023-01-29T01:31:26Z Abstract 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. Article in Journal/Newspaper Rangifer tarandus Directory of Open Access Journals: DOAJ Articles Scientific Reports 13 1 |
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Directory of Open Access Journals: DOAJ Articles |
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Medicine R Science Q |
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Medicine R Science Q Javier Lenzi Andrew F. Barnas Abdelrahman A. ElSaid Travis Desell Robert F. Rockwell Susan N. Ellis-Felege Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys |
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Medicine R Science Q |
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Abstract 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 |
Article in Journal/Newspaper |
author |
Javier Lenzi Andrew F. Barnas Abdelrahman A. ElSaid Travis Desell Robert F. Rockwell Susan N. Ellis-Felege |
author_facet |
Javier Lenzi Andrew F. Barnas Abdelrahman A. ElSaid Travis Desell Robert F. Rockwell Susan N. Ellis-Felege |
author_sort |
Javier Lenzi |
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 Portfolio |
publishDate |
2023 |
url |
https://doi.org/10.1038/s41598-023-28240-9 https://doaj.org/article/ed21980fda49496a81a363156555dc22 |
genre |
Rangifer tarandus |
genre_facet |
Rangifer tarandus |
op_source |
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023) |
op_relation |
https://doi.org/10.1038/s41598-023-28240-9 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-023-28240-9 2045-2322 https://doaj.org/article/ed21980fda49496a81a363156555dc22 |
op_doi |
https://doi.org/10.1038/s41598-023-28240-9 |
container_title |
Scientific Reports |
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13 |
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1 |
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1766175709888249856 |