Department of Computer Science, University of Wyoming, Laramie, WY 82071.
Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849.
Proc Natl Acad Sci U S A. 2018 Jun 19;115(25):E5716-E5725. doi: 10.1073/pnas.1719367115. Epub 2018 Jun 5.
Having accurate, detailed, and up-to-date information about the location and behavior of animals in the wild would improve our ability to study and conserve ecosystems. We investigate the ability to automatically, accurately, and inexpensively collect such data, which could help catalyze the transformation of many fields of ecology, wildlife biology, zoology, conservation biology, and animal behavior into "big data" sciences. Motion-sensor "camera traps" enable collecting wildlife pictures inexpensively, unobtrusively, and frequently. However, extracting information from these pictures remains an expensive, time-consuming, manual task. We demonstrate that such information can be automatically extracted by deep learning, a cutting-edge type of artificial intelligence. We train deep convolutional neural networks to identify, count, and describe the behaviors of 48 species in the 3.2 million-image Snapshot Serengeti dataset. Our deep neural networks automatically identify animals with >93.8% accuracy, and we expect that number to improve rapidly in years to come. More importantly, if our system classifies only images it is confident about, our system can automate animal identification for 99.3% of the data while still performing at the same 96.6% accuracy as that of crowdsourced teams of human volunteers, saving >8.4 y (i.e., >17,000 h at 40 h/wk) of human labeling effort on this 3.2 million-image dataset. Those efficiency gains highlight the importance of using deep neural networks to automate data extraction from camera-trap images, reducing a roadblock for this widely used technology. Our results suggest that deep learning could enable the inexpensive, unobtrusive, high-volume, and even real-time collection of a wealth of information about vast numbers of animals in the wild.
拥有关于野生动物位置和行为的准确、详细和最新信息，将提高我们研究和保护生态系统的能力。我们研究了自动、准确和廉价地收集此类数据的能力，这可能有助于推动生态学、野生动物生物学、动物学、保护生物学和动物行为学等许多领域向“大数据”科学转变。运动传感器“相机陷阱”可以廉价、不引人注意且频繁地收集野生动物照片。然而，从这些照片中提取信息仍然是一项昂贵、耗时且需要人工的任务。我们证明，这种信息可以通过深度学习（一种先进的人工智能）自动提取。我们训练深度卷积神经网络来识别、计数和描述 Snapshot Serengeti 数据集 320 万张图像中的 48 个物种的行为。我们的深度神经网络自动识别动物的准确率超过 93.8%，我们预计在未来几年内这一数字将迅速提高。更重要的是，如果我们的系统只对它有信心的图像进行分类，那么我们的系统可以在 99.3%的数据上实现自动化动物识别，同时仍能达到人工志愿者团队的 96.6%的准确率，在这个 320 万图像数据集上节省了超过 8.4 年（即 40 小时/周，超过 17000 小时）的人工标注工作。这些效率的提高凸显了使用深度神经网络从相机陷阱图像中自动提取数据的重要性，这为这项广泛使用的技术消除了一个障碍。我们的研究结果表明，深度学习可以实现廉价、不引人注意、大容量甚至实时地收集大量野生动物的大量信息。
