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Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

机译:自动识别,计数和描述野生动物   深度学习的摄像机陷阱图像

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摘要

Having accurate, detailed, and up-to-date information about the location andbehavior of animals in the wild would revolutionize our ability to study andconserve ecosystems. We investigate the ability to automatically, accurately,and inexpensively collect such data, which could transform many fields ofbiology, ecology, and zoology into "big data" sciences. Motion sensor "cameratraps" enable collecting wildlife pictures inexpensively, unobtrusively, andfrequently. However, extracting information from these pictures remains anexpensive, time-consuming, manual task. We demonstrate that such informationcan be automatically extracted by deep learning, a cutting-edge type ofartificial intelligence. We train deep convolutional neural networks toidentify, count, and describe the behaviors of 48 species in the3.2-million-image Snapshot Serengeti dataset. Our deep neural networksautomatically identify animals with over 93.8% accuracy, and we expect thatnumber to improve rapidly in years to come. More importantly, if our systemclassifies only images it is confident about, our system can automate animalidentification for 99.3% of the data while still performing at the same 96.6%accuracy as that of crowdsourced teams of human volunteers, saving more than8.4 years (at 40 hours per week) of human labeling effort (i.e. over 17,000hours) on this 3.2-million-image dataset. Those efficiency gains immediatelyhighlight the importance of using deep neural networks to automate dataextraction from camera-trap images. Our results suggest that this technologycould enable the inexpensive, unobtrusive, high-volume, and even real-timecollection of a wealth of information about vast numbers of animals in thewild.
机译:有了关于野外动物位置和行为的准确,详细和最新的信息,将会彻底改变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,这些能力可以将生物学,生态学和动物学的许多领域转变为“大数据”科学。运动传感器“摄像头”可以廉价,不显眼且经常地收集野生动物的图片。但是,从这些图片中提取信息仍然是昂贵,费时的手动任务。我们证明了可以通过深度学习(一种新型的人工智能)自动提取此类信息。我们训练深层卷积神经网络来识别,计数和描述320万图像的Snapshot Serengeti数据集中48个物种的行为。我们的深度神经网络可以自动识别动物,准确率超过93.8%,并且我们预计该数字在未来几年内会迅速提高。更重要的是,如果我们的系统仅对自己有信心的图像进行分类,则我们的系统可以自动识别99.3%的数据,同时仍具有与众筹的志愿者团队相同的96.6%的准确性,从而节省了8.4年以上的时间(在这320万张图像数据集上,每周进行40个小时的人工标记工作(即超过17,000个小时)。这些效率的提高立即突显了使用深度神经网络从相机陷印图像中自动提取数据的重要性。我们的研究结果表明,该技术可以廉价,无干扰,大批量,甚至实时收集有关野生动物数量众多的大量信息。

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