Population monitoring of colonial seabirds is often complicated by the large size of colonies, remote locations, and close inter- and intra-species aggregation. While drones have been successfully used to monitor large inaccessible colonies, the vast amount of imagery collected introduces a data analysis bottleneck. Convolutional neural networks (CNN) are evolving as a prominent means for object detection and can be applied to drone imagery for population monitoring. In this study, we explored the use of these technologies to increase capabilities for seabird monitoring by using CNNs to detect and enumerate Black-browed Albatrosses (Thalassarche melanophris) and Southern Rockhopper Penguins (Eudyptes c. chrysocome) at one of their largest breeding colonies, the Falkland (Malvinas) Islands. Our results showed that these techniques have great potential for seabird monitoring at significant and spatially complex colonies, producing accuracies of correctly detecting and counting birds at 97.66% (Black-browed Albatrosses) and 87.16% (Southern Rockhopper Penguins), with 90% of automated counts being within 5% of manual counts from imagery. The results of this study indicate CNN methods are a viable population assessment tool, providing opportunities to reduce manual labor, cost, and human error.

中文翻译：

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无人机和深度学习对大规模海鸟栖息地进行准确高效的监测

殖民地海鸟的种群监测通常因殖民地规模大、位置偏远以及物种间和物种内的紧密聚集而变得复杂。虽然无人机已成功用于监测难以接近的大型殖民地，但收集到的大量图像引入了数据分析瓶颈。卷积神经网络 (CNN) 正在发展成为一种重要的目标检测手段，可应用于无人机图像进行人口监测。在这项研究中，我们探索了使用这些技术来提高海鸟监测能力，方法是使用 CNN 在其最大的繁殖地之一检测和枚举黑眉信天翁 (Thalassarche melanophris) 和南跳岩企鹅 (Eudyptes c. chrysocome)，福克兰（马尔维纳斯）群岛。我们的研究结果表明，这些技术在重要且空间复杂的殖民地海鸟监测方面具有巨大潜力，正确检测和计数鸟类的准确率为 97.66%（黑眉信天翁）和 87.16%（南跳岩企鹅），其中 90% 的自动化计数在图像手动计数的 5% 以内。这项研究的结果表明，CNN 方法是一种可行的人口评估工具，提供了减少体力劳动、成本和人为错误的机会。