Camera traps are widely used in wildlife surveys because they are non-invasive, low-cost, and highly efficient. Camera traps deployed in the wild often produce large datasets, making it increasingly difficult to manually classify images. Deep learning is a machine learning method that provides a tool to automatically identify images, but it requires labeled training samples and high-performance servers with multiple Graphics Processing Units (GPUs). However, manually preparing large-scale training images for training deep learning models is labor intensive, and the high-performance servers with multiple GPUs are often not available for wildlife management agencies and field researchers. Our study explores an adaptive deep learning method to use small-scale training sets and a commonly-available, desktop personal computer (PC) to achieve automatic filtering of empty camera images. Our results showed that by using 29,192 training samples, the overall error, commission error, and omission error of the proposed method on a PC were 2.69%, 6.82%, and 6.45%, respectively. Moreover, the accuracy of our method can be adaptively improved on PCs in actual ecological monitoring projects, which would benefit researchers in field settings when only a PC is available. © 2021 The Wildlife Society.

中文翻译：

---

使用深度学习模型从相机陷阱中过滤空图像的自适应自动方法

相机陷阱因其非侵入性、低成本和高效性而广泛用于野生动物调查。在野外部署的相机陷阱通常会产生大型数据集，这使得手动分类图像变得越来越困难。深度学习是一种机器学习方法，可提供自动识别图像的工具，但它需要带标签的训练样本和具有多个图形处理单元 (GPU) 的高性能服务器。然而，手动准备用于训练深度学习模型的大规模训练图像是劳动密集型的，并且野生动物管理机构和实地研究人员往往无法使用具有多个 GPU 的高性能服务器。我们的研究探索了一种自适应深度学习方法，以使用小规模训练集和普遍可用的、台式个人电脑（PC）实现空相机图像的自动过滤。我们的结果表明，通过使用 29,192 个训练样本，该方法在 PC 上的总体误差、委托误差和遗漏误差分别为 2.69%、6.82% 和 6.45%。此外，我们的方法的准确性可以在实际生态监测项目中在 PC 上自适应地提高，这将有利于在只有 PC 可用的现场环境中的研究人员。© 2021 野生动物协会。当只有 PC 可用时，这将使现场环境中的研究人员受益。© 2021 野生动物协会。当只有 PC 可用时，这将使现场环境中的研究人员受益。© 2021 野生动物协会。