释放海洋生态学深度学习的潜力：概述、应用和展望
Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook
摘要：
深度学习革命正在触及所有科学学科和我们生活的角落，作为利用大数据力量的一种手段。海洋生态也不例外。这些新方法提供了对来自传感器、摄像机和声学记录器的数据的分析，即使是实时的，也是以可再现和快速的方式。现成的算法可以从数字图像或视频中找到，计数和分类物种，并检测噪声数据中的隐藏模式。利用这些机会需要跨生态和数据科学学科的合作，这可能具有挑战性。为了促进这些合作，并促进使用深度学习对基于生态系统的海洋管理，本文旨在弥合海洋生态学家和计算机科学家之间的差距。我们提供了流行的生态数据分析的深度学习方法的见解，重点是监督学习与深度神经网络的技术，并通过深度学习海洋生态学的既定和新兴应用说明挑战和机遇。我们使用的浮游生物，鱼类，海洋哺乳动物，污染和营养循环，涉及目标检测，分类，跟踪和分割的可视化数据建立和前瞻性的案例研究。最后，我们展望了该领域的机遇和挑战，包括潜在的技术进步和管理复杂数据集的问题。
英文摘要：
The deep learning revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. These new methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms can find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. Using these opportunities requires collaboration across ecological and data science disciplines, which can be challenging to initiate. To facilitate these collaborations and promote the use of deep learning towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular deep learning approaches for ecological data analysis in plain language, focusing on the techniques of supervised learning with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of deep learning to marine ecology. We use established and future-looking case studies on plankton, fishes, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field's opportunities and challenges, including potential technological advances and issues with managing complex data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
