When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images
Open accessAbstract
Automated object detectors on Unmanned Aerial Vehicles (UAVs) are increasingly employed for a wide range of tasks. However, to be accurate in their specific task they need expensive ground truth in the form of bounding boxes or positional information. Weakly-Supervised Object Detection (WSOD) overcomes this hindrance by localizing objects with only image-level labels that are faster and cheaper to obtain, but is not on par with fully-supervised models in terms of performance. In this study we propose to combine both approaches in a model that is principally apt for WSOD, but receives full position ground truth for a small number of images. Experiments show that with just 1% of densely annotated images, but simple image-level counts as remaining ground truth, we effectively match the performance of fully-supervised models on a challenging dataset with scarcely occurring wildlife on UAV images from the African savanna. As a result, with a very limited amount of precise annotations our model can be trained with ground truth that is orders of magnitude cheaper and faster to obtain while still providing the same detection performance.
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Large mammal detection in the Kuzikus reserve in 2014 was improved from a recall rate of 20% to 80% for a common 75% precision rate.
“large mammal detection in the Kuzikus reserve in 2014 was improved significantly by improving the detection methodologies, from a recall rate of 20%35 to 80%37 (for a common 75% precision rate).”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [37]
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Cited by
Sorted by citations
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740 · see citing works
Self-citation
- Authors
- Kellenberger, B., Marcos, D., & Tuia, D.
- Affiliations
- Wageningen University & Research (NL)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1414-1422
- OpenAlex
- W2953098847
- Cited by
- 33
- References
- 37
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 5 · 2023: 5 · 2022: 7 · 2021: 6 · 2020: 6
- FWCI
- 1.55
- Type
- conference paper
- Language
- en
- Open access
- green
- research.wur.nl
- Licence
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Socio-Environmental Systems Modeling (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- weakly supervised object detection, wildlife detection, unmanned aerial vehicles, UAV imagery, image-level labels, bounding box annotation, annotation cost reduction, African savanna
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