Cited by 335Advanced Neural Network ApplicationsWildlife Ecology and ConservationSmart Agriculture and AI

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Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning

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Kellenberger, Benjamin, Marcos, Diego, Tuia, Devis·2018·Remote Sensing of Environment

DOI 10.1016/j.rse.2018.06.028

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Abstract

Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large datasets from computer vision. However, the majority of works related to wildlife focuses only on small datasets (typically subsets of UAV campaigns), which might be detrimental when presented with the sheer scale of real study areas for large mammal census. Methods may yield thousands of false alarms in such cases. In this paper, we study how to scale CNNs to large wildlife census tasks and present a number of recommendations to train a CNN on a large UAV dataset. We further introduce novel evaluation protocols that are tailored to censuses and model suitability for subsequent human verification of detections. Using our recommendations, we are able to train a CNN reducing the number of false positives by an order of magnitude compared to previous state-of-the-art. Setting the requirements at 90% recall, our CNN allows to reduce the amount of data required for manual verification by three times, thus making it possible for rangers to screen all the data acquired efficiently and to detect almost all animals in the reserve automatically.

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Authors: Kellenberger, B., Marcos, D., & Tuia, D.
Affiliations: Wageningen University & Research (NL)
Corresponding author: Kellenberger, Benjamin
ORCID: Kellenberger, Benjamin
Marcos, Diego
Tuia, Devis
Venue: Remote Sensing of Environment
Publisher: Elsevier BV
Published: 2018-10
Volume: 216
Pages: 139-153
ISSN: 0034-4257 (ISSN-L)
DOI: 10.1016/j.rse.2018.06.028
OpenAlex: W2810030371
Cited by: 335
References: 49
Citations by year: 2026: 39 · 2025: 35 · 2024: 44 · 2023: 53 · 2022: 40 · 2021: 53 · 2020: 37 · 2019: 34
FWCI: 15.51
Type: journal article
Language: en
Open access: green
research.wur.nl
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Remote Sensing of Environment (publishedVersion)
Socio-Environmental Systems Modeling (acceptedVersion, cc-by-nc-nd)
arXiv (Cornell University) (submittedVersion)
Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
arXiv (Cornell University)
Funders: Schweizerischer Nationalfonds zur FÃ¶rderung der Wissenschaftlichen Forschung (PZ00P2-136827)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
Keywords: mammal detection, unmanned aerial vehicles, wildlife census, class imbalance, convolutional neural networks, object detection, false positive reduction, evaluation protocol, human verification, large-scale image databases, wildlife reserves, livestock monitoring

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Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning

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