CoralscapesV2：珊瑚礁中的全景与细粒度视觉场景理解
CoralscapesV2: Panoptic and Fine-Grained Visual Scene Understanding in Coral Reefs
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中文总结 AI 辅助
本文提出CoralscapesV2，扩展珊瑚礁数据集至95类细粒度类别和65k鱼类实例标注，首个全景分割基准，助力通用珊瑚礁监测。
中文摘要 AI 辅助
为了设计保护和恢复策略以应对全球珊瑚礁的衰退，珊瑚礁的生态监测需要大幅扩展。计算机视觉方法越来越多地被用于处理大量数据：随着珊瑚礁数据收集范式从高度标准化和受限的调查图像转向可扩展平台上的无约束图像，有必要设计机器学习方法，帮助从通用珊瑚礁图像中获得对珊瑚礁的细粒度理解。本文提供了CoralscapesV2，这是Coralscapes数据集的扩展，用于珊瑚礁中的通用视觉场景理解。CoralscapesV2增加了语义分割的数据集规模、范围、标签完整性和质量，并将类别数量从39个细粒度视觉类别扩展到95个。此外，CoralscapesV2提供了65k个详尽的鱼类实例掩码标注，通过使用视频精心标注至完整，揭示了仅基于静态图像的鱼类标注是不充分的。CoralscapesV2是首个用于珊瑚礁全景分割的数据集，捕获了野外广泛的情景，为当代语义分割和实例分割模型提出了一个具有挑战性的基准。CoralscapesV2是迈向珊瑚礁通用全景分割的重要一步，对扩大珊瑚礁监测具有重大意义，因为它可以应用于从机器人或手持视频的底栖覆盖制图到设计自动量化和理解鱼类行为及鱼类-珊瑚礁相互作用的方法等广泛的应用。
英文摘要
In order to design conservation and restoration strategies to counter the global decline of coral reefs, ecological monitoring of reefs needs to be scaled up dramatically. Computer vision methods are increasingly used to tackle the vast amount of data: as the paradigm of data collection in reefs shifts from highly standardized and constrained survey images to unconstrained imagery on scalable platforms, it is necessary to design machine learning methods that help to get a fine-grained understanding of reefs from general-purpose reef imagery. This paper provides CoralscapesV2, an extension of the Coralscapes dataset for general-purpose visual scene understanding in reefs. CoralscapesV2 increases the dataset size, scope, label completeness and quality for semantic segmentation, and extends the number of classes from 39 to 95 fine-grained visual categories. Furthermore, CoralscapesV2 provides 65k exhaustive fish instance mask annotations, meticulously annotated to completeness by using the video, revealing that annotation of fish based on only static images is insufficient. CoralscapesV2 is the first dataset for panoptic segmentation in coral reefs, capturing a wide range of scenarios in the wild, posing a challenging benchmark for contemporary semantic segmentation and instance segmentation models. CoralscapesV2 is an important step towards general-purpose panoptic segmentation in coral reefs, which has substantial implications for scaling up coral reef monitoring, as it can be employed in a wide range of applications from benthic cover mapping from robot or handheld videos to designing methods for automated quantification and understanding of fish behavior and fish-reef interactions.
发表机构
- MIT（麻省理工学院）
- EPFL（洛桑联邦理工学院）
- University of Djibouti（吉布提大学）
- Ministry of Environment and Sustainable Development of Djibouti（吉布提环境与可持续发展部）
- Centre d’Études et de Recherche de Djibouti（吉布提研究与研究中心）
- Red Sea University of Port Sudan（苏丹港红海大学）
- Aqaba Marine Reserve, Jordan（约旦亚喀巴海洋保护区）
- United Nations Development Programme Jordan（联合国开发计划署约旦办事处）
- Mai Nefhi College of Science, Eritrea（厄立特里亚马伊内菲理学院）
- Ministry of Marine Resources, Massawa, Eritrea（厄立特里亚马萨瓦海洋资源部）
机构由 AI 辅助整理，请以论文原文为准。
