Artificial intelligence has the potential to make valuable contributions to wildlife management through cost-effective methods for the collection and interpretation of wildlife data. Recent advances in remotely piloted aircraft systems (RPAS or ``drones'') and thermal imaging technology have created new approaches to collect wildlife data. These emerging technologies could provide promising alternatives to standard labourious field techniques as well as cover much larger areas. In this study, we conduct a comprehensive review and empirical study of drone-based wildlife detection. Specifically, we collect a realistic dataset of drone-derived wildlife thermal detections. Wildlife detections, including arboreal (for instance, koalas, phascolarctos cinereus) and ground dwelling species in our collected data are annotated via bounding boxes by experts. We then benchmark state-of-the-art object detection algorithms on our collected dataset. We use these experimental results to identify issues and discuss future directions in automatic animal monitoring using drones.

翻译：人工智能通过经济高效的方法收集和解读野生动物数据，有望为野生动物管理做出宝贵贡献。近年来，远程驾驶航空系统（RPAS或"无人机"）和热成像技术的进步为收集野生动物数据创造了新途径。这些新兴技术可能为传统繁重的野外工作方法提供有前景的替代方案，并能覆盖更广阔的区域。在本研究中，我们对基于无人机的野生动物检测进行了全面综述和实证研究。具体而言，我们收集了真实的无人机热成像野生动物检测数据集。由专家通过边界框对数据集中采集的野生动物检测结果（包括树栖物种如考拉、袋貂以及地面栖息物种）进行了标注。随后，我们在该数据集上对最先进的目标检测算法进行了基准测试。基于这些实验结果，我们指出了当前存在的问题，并探讨了利用无人机进行自动动物监测的未来发展方向。