Advances in underwater imaging enable collection of extensive seafloor image datasets necessary for monitoring important benthic ecosystems. The ability to collect seafloor imagery has outpaced our capacity to analyze it, hindering mobilization of this crucial environmental information. Machine learning approaches provide opportunities to increase the efficiency with which seafloor imagery is analyzed, yet large and consistent datasets 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 3.1 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 reuse at https://doi.org/10.20383/103.0614.

翻译：水下成像技术的进步使得收集用于监测重要底栖生态系统所需的大规模海底图像数据集成为可能。采集海底图像的能力已超越了我们分析这些图像的能力，阻碍了这一关键环境信息的有效利用。机器学习方法为提高海底图像分析效率提供了机遇，然而支持此类方法开发的大型且一致的数据集却十分稀缺。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，我们使用其中具有代表性的130万张子集来呈现多样化的海底环境。这些图像配有310万个按CATAMI标准转换的标注，覆盖了19万张图像。基于该汇编训练的大型深度学习模型初步结果表明，其在自动化大规模与小规模图像分析任务方面具有实用价值。该汇编数据与模型已在https://doi.org/10.20383/103.0614公开提供以供复用。