Fast animal detection in UAV images using convolutional neural networks

Benjamin Kellenberger, Michele Volpi, Devis Tuia

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

15 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
Place of PublicationFort Worth
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages866-869
Number of pages4
ISBN (Electronic)9781509049516
ISBN (Print)9781509049523
DOIs
Publication statusPublished - 1 Dec 2017
Externally publishedYes
Event37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017-July

Conference

Conference37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
CountryUnited States
CityFort Worth
Period23/07/1728/07/17

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