Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning
Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning
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DOI：
10.3389/fmars.2020.00429
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发表时间：
2020-06-05
影响因子：
3.7
通讯作者：
Connolly, Rod M.
中科院分区：
文献类型：
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作者：
Ditria, Ellen M.;Lopez-Marcano, Sebastian;Connolly, Rod M.
水生生态学家经常对动物进行计数，为保护和管理提供关键信息。越来越多的水下记录设备（如运动摄像机和无人水下设备）可以有效安全地捕捉镜头，而无需手动数据收集通常存在的后勤困难。然而，它导致了大量的数据被收集，需要人工处理，因此需要大量的时间、人力和金钱。使用深度学习自动化图像处理具有实质性的好处，但很少在水生生态学领域采用。为了测试其有效性和实用性，我们将深度学习技术的准确性和速度与人类同行进行了比较，以量化水下图像和视频片段中的鱼类丰度。我们收集了澳大利亚昆士兰州海草草地上鱼群聚集的镜头。我们使用目标检测框架制作了三个模型来检测目标物种，一种生态上重要的鱼，luderick （Girella tricuspidata）。我们的模型在三个随机的80:20比率的训练：验证数据集上进行训练，这些数据集来自总共6080个注释。计算机使用来自与训练数据相同的河口的未见镜头（F1 = 92.4%, mAP50 = 92.5%）和来自不同河口的新镜头（F1 = 92.3%, mAP50 = 93.4%）准确地从高性能视频中确定了丰度。在单个图像测试数据集中，计算机在确定丰度方面的表现比人类海洋专家高出7.1%，比公民科学家高出13.4%，在视频数据集中分别高出1.5%和7.8%。我们表明，在确定丰度方面，深度学习可以成为比人类更准确的工具，并且结果在调查地点之间是一致和可转移的。深度学习方法提供了一种更快、更便宜、更准确的替代人工数据分析方法，目前用于监测和评估动物丰度，并为水生生态领域提供了很多帮助。
Aquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as action cameras and unmanned underwater devices has allowed footage to be captured efficiently and safely, without the logistical difficulties manual data collection often presents. It has, however, led to immense volumes of data being collected that require manual processing and thus significant time, labor, and money. The use of deep learning to automate image processing has substantial benefits but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using an object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomized 80:20 ratios of training:validation datasets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%) and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computer's performance in determining abundance was 7.1% better than human marine experts and 13.4% better than citizen scientists in single image test datasets, and 1.5 and 7.8% higher in video datasets, respectively. We show that deep learning can be a more accurate tool than humans at determining abundance and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper, and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance and have much to offer the field of aquatic ecology.