Abstract
Marine ecosystems face increasing pressure due to climate change, driving the need for scalable, AI-powered monitoring solutions. This paper examines the rapid emergence of underwater AI as a major research frontier and analyzes the factors that have transformed marine perception from a niche application into a catalyst for AI innovation. We identify three convergent drivers: environmental necessity for ecosystem-scale monitoring, democratization of underwater datasets through citizen science platforms, and researcher migration from saturated terrestrial computer vision domains. Our analysis reveals how unique underwater challenges - turbidity, cryptic species detection, expert annotation bottlenecks, and cross-ecosystem generalization - are driving fundamental advances in weakly supervised learning, open-set recognition, and robust perception under degraded conditions. We survey emerging trends in datasets, scene understanding and 3D reconstruction, highlighting the paradigm shift from passive observation toward AI-driven, targeted intervention capabilities. The paper demonstrates how underwater constraints are pushing the boundaries of foundation models, self-supervised learning, and perception, with methodological innovations that extend far beyond marine applications to benefit general computer vision, robotics, and environmental monitoring.
Abstract (translated)
海洋生态系统正面临气候变化带来的日益增加的压力，这推动了可扩展、人工智能（AI）驱动的监测解决方案的需求。本文探讨了水下AI这一重要研究领域的迅速崛起，并分析了将海洋感知从一个利基应用转变为推动AI创新催化剂的各种因素。我们识别出三个汇聚驱动因素：生态系统规模监测所需的环境必要性；通过公民科学平台实现水下数据集的民主化；以及研究人员从饱和的陆地计算机视觉领域向水下研究领域的迁移。 我们的分析揭示了独特的水下挑战——浑浊度、隐秘物种检测、专家注释瓶颈和跨生态系统的泛化能力，这些挑战正在推动弱监督学习、开放集识别及降级条件下稳健感知等基础性的进步。我们概述了数据集、场景理解和三维重建等领域中的新兴趋势，强调从被动观察向AI驱动的有针对性干预能力转变的范式变化。 本文展示了水下约束条件如何在边界上推进基础模型、自监督学习和感知方法的发展，并且这些方法创新远远超出了海洋应用范围，对通用计算机视觉、机器人技术和环境监测领域都带来了益处。
URL
https://arxiv.org/abs/2509.01878
