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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Bibliographic Details
Published in:Drones
Main Authors: Jefferson T. Hinke, Louise M. Giuseffi, Victoria R. Hermanson, Samuel M. Woodman, Douglas J. Krause
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2022
Subjects:
automated detection
Antarctica
drone
census
image analysis
Motor vehicles. Aeronautics. Astronautics
TL1-4050
Antarctic
Livingston Island
Shirreff
Cape Shirreff
Antarc*
Antarctic Fur Seals
Arctocephalus gazella
Online Access:https://doi.org/10.3390/drones6090255
https://doaj.org/article/d29ec75c33e74e18b85e4dec66cf01a4
Description
Summary: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.

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