Deep object detection for waterbird monitoring using aerial imagery Article Swipe
Krish Kabra
,
Alexander Xiong
,
Wenbin Li
,
Minxuan Luo
,
William W. Lu
,
Raúl García
,
Dhananjay Vijay
,
Jiahui Yu
,
Maojie Tang
,
Tianjiao Yu
,
Hank Arnold
,
Anna Vallery
,
Richard J. Gibbons
,
Arko Barman
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.04868
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.04868
Monitoring of colonial waterbird nesting islands is essential to tracking waterbird population trends, which are used for evaluating ecosystem health and informing conservation management decisions. Recently, unmanned aerial vehicles, or drones, have emerged as a viable technology to precisely monitor waterbird colonies. However, manually counting waterbirds from hundreds, or potentially thousands, of aerial images is both difficult and time-consuming. In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone. By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast. Our experiments using Faster R-CNN and RetinaNet object detectors give mean interpolated average precision scores of 67.9% and 63.1% respectively.
Related Topics
Concepts
Drone
Aerial survey
Object detection
Aerial imagery
Pipeline (software)
Artificial intelligence
Convolutional neural network
Geography
Deep learning
Computer science
Object (grammar)
Citizen science
Computer vision
Cartography
Remote sensing
Pattern recognition (psychology)
Biology
Programming language
Genetics
Botany
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.04868
- https://arxiv.org/pdf/2210.04868
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4304732329
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4304732329Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.04868Digital Object Identifier
- Title
-
Deep object detection for waterbird monitoring using aerial imageryWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-10Full publication date if available
- Authors
-
Krish Kabra, Alexander Xiong, Wenbin Li, Minxuan Luo, William W. Lu, Raúl García, Dhananjay Vijay, Jiahui Yu, Maojie Tang, Tianjiao Yu, Hank Arnold, Anna Vallery, Richard J. Gibbons, Arko BarmanList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.04868Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2210.04868Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2210.04868Direct OA link when available
- Concepts
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Drone, Aerial survey, Object detection, Aerial imagery, Pipeline (software), Artificial intelligence, Convolutional neural network, Geography, Deep learning, Computer science, Object (grammar), Citizen science, Computer vision, Cartography, Remote sensing, Pattern recognition (psychology), Biology, Programming language, Genetics, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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