Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife
monitoring, with several advantages over traditional field-based methods. They
have readily been used to count birds, marine mammals and large herbivores in
different environments, tasks which are routinely performed through manual
counting in large collections of images. In this paper, we propose a
semi-automatic system able to detect large mammals in semi-arid Savanna. It
relies on an animal-detection system based on machine learning, trained with
crowd-sourced annotations provided by volunteers who manually interpreted
sub-decimeter resolution color images. The system achieves a high recall rate
and a human operator can then eliminate false detections with limited effort.
Our system provides good perspectives for the development of data-driven
management practices in wildlife conservation. It shows that the detection of
large mammals in semi-arid Savanna can be approached by processing data
provided by standard RGB cameras mounted on affordable fixed wings UAVs.

本研究提出了一种基于机器学习的动物检测系统，采用群众智慧提供的数据，在半干旱的热带草原上检测大型哺乳动物，通过固定翼的无人机上的标准 RGB 相机提供数据，为野生动物保护提供了数据驱动的管理实践的转型方案。