Advances in underwater imaging enable the collection of extensive seafloor image datasets that are necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering expedient mobilization of this crucial environmental information. Recent machine learning approaches provide opportunities to increase the efficiency with which seafloor image datasets are analyzed, yet large and consistent datasets necessary to support development of such approaches are scarce. Here we present BenthicNet: a global compilation of seafloor imagery designed to support the training and evaluation of large-scale image recognition models. An initial set of over 11.4 million images was collected and curated to represent a diversity of seafloor environments using a representative subset of 1.3 million images. These are accompanied by 2.6 million annotations translated to the CATAMI scheme, which span 190,000 of the images. A large deep learning model was trained on this compilation and preliminary results suggest it has utility for automating large and small-scale image analysis tasks. The compilation and model are made openly available for use by the scientific community at https://doi.org/10.20383/103.0614.

翻译：水下成像技术的进步使得收集大规模海底图像数据集成为可能，这对于监测重要的底栖生态系统至关重要。海底图像采集能力已超越我们对其进行分析的能力，阻碍了这一关键环境信息的及时利用。近期的机器学习方法为提高海底图像数据集的分析效率提供了机遇，然而支持此类方法开发所需的大规模且一致的数据集仍然稀缺。本文提出BenthicNet：一个旨在支持大规模图像识别模型训练与评估的全球海底图像汇编。通过收集并筛选超过1140万张初始图像，选取其中具有代表性的130万张图像构建数据集，以体现海底环境的多样性。该数据集包含基于CATAMI标准转换的260万条标注，覆盖19万张图像。基于此汇编数据训练的大型深度学习模型初步结果表明，该模型对于自动化大规模与小规模图像分析任务具有实用价值。汇编数据与模型已通过 https://doi.org/10.20383/103.0614 向科学界公开。