Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images manually. Artificial intelligence systems can take over this task but usually need a large number of already-labeled training images to achieve sufficient performance. This requirement necessitates human expert labor and poses a particular challenge for projects with few cameras or short durations. We propose a label-efficient learning strategy that enables researchers with small or medium-sized image databases to leverage the potential of modern machine learning, thus freeing crucial resources for subsequent analyses. Our methodological proposal is two-fold: (1) We improve current strategies of combining object detection and image classification by tuning the hyperparameters of both models. (2) We provide an active learning (AL) system that allows training deep learning models very efficiently in terms of required human-labeled training images. We supply a software package that enables researchers to use these methods directly and thereby ensure the broad applicability of the proposed framework in ecological practice. We show that our tuning strategy improves predictive performance. We demonstrate how the AL pipeline reduces the amount of pre-labeled data needed to achieve a specific predictive performance and that it is especially valuable for improving out-of-sample predictive performance. We conclude that the combination of tuning and AL increases predictive performance substantially. Furthermore, we argue that our work can broadly impact the community through the ready-to-use software package provided. Finally, the publication of our models tailored to European wildlife data enriches existing model bases mostly trained on data from Africa and North America.

翻译：野生动物相机陷阱图像被广泛用于研究动物丰度、栖息地关联及行为，但这一过程因需专家手动分类图像而变得复杂。人工智能系统可承担此任务，但通常需要大量已标注的训练图像才能达到足够性能，这一要求不仅依赖人类专家劳动，还对相机数量少或研究周期短的项目构成特殊挑战。我们提出一种标签高效的学习策略，使中小型图像数据库的研究者能够充分利用现代机器学习潜力，从而将关键资源释放至后续分析。方法论包含两个方面：(1) 通过调优目标检测与图像分类两个模型的超参数，改进当前将两者结合的策略；(2) 提供一种主动学习系统，使深度学习模型的训练在所需人工标注图像数量方面极为高效。我们提供可直接应用这些方法的软件包，从而确保所提框架在生态实践中的广泛适用性。实验表明，调优策略提升了预测性能。我们展示了主动学习流水线如何减少达到特定预测性能所需的预标注数据量，并特别强调其在提升样本外预测性能方面的价值。结论指出，调优与主动学习的结合显著提升了预测性能。此外，我们认为通过提供即用型软件包，本研究可对学界产生广泛影响。最后，针对欧洲野生动物数据定制的模型发表，将丰富现有主要基于非洲与北美数据训练的模型库。