Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning

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

304 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1006a4a4499902264887aa0c0a4cfb743ca065c5

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In Computer Vision and Pattern Recognition

Citations in the corpus

Unmanned Aerial Vehicles (UAVs) in environmental biology: a review (2019, methodology): “The data from this study were used by Kellenberger et al. (2018) who proposed another semi-automatic detection system using convolutional neural networks (CNNs), which are one of the deep learning algorithms.”
Perspectives in machine learning for wildlife conservation (2021, background): “ing (29, 30, 79), semi-automatic detection of large mammals (48, 80), birds (81) and, in low altitude flight, even identification of individuals (82).”