Machine learning to classify animal species in camera trap images: applications in ecology

Motion‐activated cameras (“camera traps”) are increasingly used in ecological and management studies for remotely observing wildlife and are amongst the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analysed, typically by visually observing each image, in order to extract data that can be used in ecological analyses. We trained machine learning models using convolutional neural networks with the ResNet‐18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out‐of‐sample (or “out‐of‐distribution” in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out‐of‐sample dataset fr...运动触发相机（camera traps）正日益广泛应用于生态学与管理研究中，用于远程监测野生动物，是当前野生动物研究领域最有力的工具之一。然而，采用相机陷阱的研究会产生数百万张图像，往往需要研究人员逐张目视检视，方可提取可用于生态学分析的相关数据。本研究采用卷积神经网络（convolutional neural networks）结合残差网络-18（ResNet-18）架构，依托3,367,383张图像训练机器学习模型，以实现对美国5个州采集的相机陷阱图像中的野生动物物种进行自动分类。随后，我们分别在两个独立测试集上验证模型性能：其一为训练阶段未见过的美国本土独立图像子集，其二为来自加拿大的有蹄类动物图像样本外（机器学习领域又称"分布外"）数据集。此外，我们还在另一样本外数据集上测试了模型区分空图像与含动物图像的能力，相关数据来自fr……