When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

Automated object detectors on Unmanned Aerial Vehi-cles (UAVs) are increasingly employed for a wide rangeof tasks. However, to be accurate in their specific taskthey need expensive ground truth in the form of boundingboxes or positional information. Weakly-Supervised Ob-ject Detection (WSOD) overcomes this hindrance by local-izing objects with only image-level labels that are faster andcheaper to obtain, but is not on par with fully-supervisedmodels in terms of performance. In this study we proposeto combine both approaches in a model that is principallyapt for WSOD, but receives full position ground truth fora small number of images. Experiments show that withjust 1% of densely annotated images, but simple image-level counts as remaining ground truth, we effectively matchthe performance of fully-supervised models on a challeng-ing dataset with scarcely occurring wildlife on UAV imagesfrom the African savanna. As a result, with a very limitedamount of precise annotations our model can be trainedwith ground truth that is orders of magnitude cheaper andfaster to obtain while still providing the same detection per-formance.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
Publication statusE-pub ahead of print - 2019

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Kellenberger, B. A., Marcos Gonzalez, D., & Tuia, D. (2019). When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops Institute of Electrical and Electronics Engineers Inc..
Kellenberger, B.A. ; Marcos Gonzalez, D. ; Tuia, D. / When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Institute of Electrical and Electronics Engineers Inc., 2019.
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title = "When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images",
abstract = "Automated object detectors on Unmanned Aerial Vehi-cles (UAVs) are increasingly employed for a wide rangeof tasks. However, to be accurate in their specific taskthey need expensive ground truth in the form of boundingboxes or positional information. Weakly-Supervised Ob-ject Detection (WSOD) overcomes this hindrance by local-izing objects with only image-level labels that are faster andcheaper to obtain, but is not on par with fully-supervisedmodels in terms of performance. In this study we proposeto combine both approaches in a model that is principallyapt for WSOD, but receives full position ground truth fora small number of images. Experiments show that withjust 1{\%} of densely annotated images, but simple image-level counts as remaining ground truth, we effectively matchthe performance of fully-supervised models on a challeng-ing dataset with scarcely occurring wildlife on UAV imagesfrom the African savanna. As a result, with a very limitedamount of precise annotations our model can be trainedwith ground truth that is orders of magnitude cheaper andfaster to obtain while still providing the same detection per-formance.",
author = "B.A. Kellenberger and {Marcos Gonzalez}, D. and D. Tuia",
year = "2019",
language = "English",
booktitle = "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

Kellenberger, BA, Marcos Gonzalez, D & Tuia, D 2019, When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Institute of Electrical and Electronics Engineers Inc.

When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images. / Kellenberger, B.A.; Marcos Gonzalez, D.; Tuia, D.

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Institute of Electrical and Electronics Engineers Inc., 2019.

Research output: Chapter in Book/Report/Conference proceedingConference paper

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T1 - When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images

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N2 - Automated object detectors on Unmanned Aerial Vehi-cles (UAVs) are increasingly employed for a wide rangeof tasks. However, to be accurate in their specific taskthey need expensive ground truth in the form of boundingboxes or positional information. Weakly-Supervised Ob-ject Detection (WSOD) overcomes this hindrance by local-izing objects with only image-level labels that are faster andcheaper to obtain, but is not on par with fully-supervisedmodels in terms of performance. In this study we proposeto combine both approaches in a model that is principallyapt for WSOD, but receives full position ground truth fora small number of images. Experiments show that withjust 1% of densely annotated images, but simple image-level counts as remaining ground truth, we effectively matchthe performance of fully-supervised models on a challeng-ing dataset with scarcely occurring wildlife on UAV imagesfrom the African savanna. As a result, with a very limitedamount of precise annotations our model can be trainedwith ground truth that is orders of magnitude cheaper andfaster to obtain while still providing the same detection per-formance.

AB - Automated object detectors on Unmanned Aerial Vehi-cles (UAVs) are increasingly employed for a wide rangeof tasks. However, to be accurate in their specific taskthey need expensive ground truth in the form of boundingboxes or positional information. Weakly-Supervised Ob-ject Detection (WSOD) overcomes this hindrance by local-izing objects with only image-level labels that are faster andcheaper to obtain, but is not on par with fully-supervisedmodels in terms of performance. In this study we proposeto combine both approaches in a model that is principallyapt for WSOD, but receives full position ground truth fora small number of images. Experiments show that withjust 1% of densely annotated images, but simple image-level counts as remaining ground truth, we effectively matchthe performance of fully-supervised models on a challeng-ing dataset with scarcely occurring wildlife on UAV imagesfrom the African savanna. As a result, with a very limitedamount of precise annotations our model can be trainedwith ground truth that is orders of magnitude cheaper andfaster to obtain while still providing the same detection per-formance.

M3 - Conference paper

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Kellenberger BA, Marcos Gonzalez D, Tuia D. When a Few Clicks Make All the Difference: Improving Weakly-Supervised Wildlife Detection in UAV Images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Institute of Electrical and Electronics Engineers Inc. 2019