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Fast animal detection in UAV images using convolutional neural networks


Kellenberger, Benjamin; Volpi, Michele; Tuia, Devis (2017). Fast animal detection in UAV images using convolutional neural networks. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, 23 July 2017 - 28 July 2017, 866-869.

Abstract

Illegal wildlife poaching poses one severe threat to the environment. Measures to stem poaching have only been with limited success, mainly due to efforts required to keep track of wildlife stock and animal tracking. Recent developments in remote sensing have led to low-cost Unmanned Aerial Vehicles (UAVs), facilitating quick and repeated image acquisitions over vast areas. In parallel, progress in object detection in computer vision yielded unprecedented performance improvements, partially attributable to algorithms like Convolutional Neural Networks (CNNs). We present an object detection method tailored to detect large animals in UAV images. We achieve a substantial increase in precision over a robust state-of-the-art model on a dataset acquired over the Kuzikus wildlife reserve park in Namibia. Furthermore, our model processes data at over 72 images per second, as opposed 3 for the baseline, allowing for real-time applications.

Abstract

Illegal wildlife poaching poses one severe threat to the environment. Measures to stem poaching have only been with limited success, mainly due to efforts required to keep track of wildlife stock and animal tracking. Recent developments in remote sensing have led to low-cost Unmanned Aerial Vehicles (UAVs), facilitating quick and repeated image acquisitions over vast areas. In parallel, progress in object detection in computer vision yielded unprecedented performance improvements, partially attributable to algorithms like Convolutional Neural Networks (CNNs). We present an object detection method tailored to detect large animals in UAV images. We achieve a substantial increase in precision over a robust state-of-the-art model on a dataset acquired over the Kuzikus wildlife reserve park in Namibia. Furthermore, our model processes data at over 72 images per second, as opposed 3 for the baseline, allowing for real-time applications.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:28 July 2017
Deposited On:23 Mar 2018 15:07
Last Modified:13 Apr 2018 11:47
Publisher:IEEE
ISBN:978-1-5090-4951-6
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/IGARSS.2017.8127090

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