21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning
21 000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning
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4.5 小时内检测到 21 000 只鸟类：利用机器学习进行高效的大规模海鸟检测
DOI：
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发表时间：
2021
影响因子：
5.5
通讯作者：
D. Tuia
中科院分区：
文献类型：
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作者：
B. Kellenberger;Thor Veen;Eelke Folmer;D. Tuia
研究了利用深度卷积神经网络对无人机图像中的海鸟进行自动检测和计数的问题。我们的研究区域，西非海岸，有大量繁殖的燕鸥和海鸥，它们是食物网中的顶级捕食者，是海洋生态系统健康的重要生物指标。到目前为止，估计繁殖数量的调查一直是徒步进行的，这既繁琐，又不准确，而且会造成干扰。通过使用无人机和CNN，允许自动定位数万只鸟，我们展示了所有三个限制都可以优雅地解决。由于我们采用了轻量级的CNN架构，并融入了有关鸟类在群体内的空间分布的先验知识，因此我们能够将CNN培训所需的鸟类注释数量减少到每节课仅200个示例。我们的模型对于最丰富的皇家燕鸥物种获得了良好的准确率(90%的准确率在90%的召回率下)，但对于较稀有的里海燕鸥和海鸥物种的准确率较低(60%的准确率在68%的召回率下，20%的准确率在88%的召回率)，这相当于所有在场的个体的7%左右。总而言之，我们的结果表明，我们可以在短短4.5小时内，从开始到结束，对21 000只鸟类中的大多数进行检测和分类，而不是用大约3周的时间来繁琐地识别和标记所有鸟类。
We address the task of automatically detecting and counting seabirds in unmanned aerial vehicle (UAV) imagery using deep convolutional neural networks (CNNs). Our study area, the coast of West Africa, harbours significant breeding colonies of terns and gulls, which as top predators in the food web function as important bioindicators for the health of the marine ecosystem. Surveys to estimate breeding numbers have hitherto been carried out on foot, which is tedious, imprecise and causes disturbance. By using UAVs and CNNs that allow localizing tens of thousands of birds automatically, we show that all three limitations can be addressed elegantly. As we employ a lightweight CNN architecture and incorporate prior knowledge about the spatial distribution of birds within the colonies, we were able to reduce the number of bird annotations required for CNN training to just 200 examples per class. Our model obtains good accuracy for the most abundant species of royal terns (90% precision at 90% recall), but is less accurate for the rarer Caspian terns and gull species (60% precision at 68% recall, respectively 20% precision at 88% recall), which amounts to around 7% of all individuals present. In sum, our results show that we can detect and classify the majority of 21 000 birds in just 4.5 h, start to finish, as opposed to about 3 weeks of tediously identifying and labelling all birds by hand.