Abstract Antarctic pack-ice seals, a group of four species of true seals (Phocidae), play a pivotal role in the Southern Ocean foodweb as wide-ranging predators of Antarctic krill (Euphausia superba). Due to their circumpolar distribution and the remoteness and vastness of their habitat, little is known about their population sizes. Estimating pack-ice seal population sizes and trends is key to understanding how the Southern Ocean ecosystem will react to threats such as climate change driven sea ice loss and krill fishing. We present a functional pack-ice seal detection pipeline using Worldview-3 imagery and a Convolutional Neural Network that counts and locates seal centroids. We propose a new CNN architecture that detects objects by combining semantic segmentation heatmaps with binary classification and counting by regression. Our pipeline locates over 30% of seals, when compared to consensus counts from human experts, and reduces the time required for seal detection by 95% (assuming just a single GPU). While larger training sets and continued algorithm development will no doubt improve classification accuracy, our pipeline, which can be easily adapted for other large-bodied animals visible in sub-meter satellite imagery, demonstrates the potential for machine learning to vastly expand our capacity for regular pack-ice seal surveys and, in doing so, will contribute to ongoing international efforts to monitor pack-ice seals.

中文翻译：

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SealNet：用于亚米级卫星图像的全自动浮冰密封检测管道

摘要 南极浮冰海豹是一组四种真正的海豹 (Phocidae)，作为南极磷虾 (Euphausia superba) 的广泛捕食者，在南大洋食物网中发挥着举足轻重的作用。由于它们的极地分布以及栖息地的偏远和广阔，人们对其种群规模知之甚少。估计浮冰海豹种群规模和趋势是了解南大洋生态系统如何应对气候变化导致的海冰损失和磷虾捕捞等威胁的关键。我们使用 Worldview-3 图像和计算和定位海豹质心的卷积神经网络提出了一个功能性浮冰海豹检测管道。我们提出了一种新的 CNN 架构，通过将语义分割热图与二元分类和回归计数相结合来检测对象。与人类专家的共识计数相比，我们的管道定位了超过 30% 的密封件，并将密封件检测所需的时间减少了 95%（假设只有一个 GPU）。虽然更大的训练集和持续的算法开发无疑会提高分类精度，但我们的管道可以轻松适应亚米级卫星图像中可见的其他大型动物，展示了机器学习的潜力，可以极大地扩展我们的常规能力。浮冰封印调查，并在此过程中将有助于正在进行的国际努力监测浮冰封印。