Expert, crowd, students or algorithm: who holds the key to deep-sea imagery ‘big data’ processing?

1. Recent technological development has increased our capacity to study the deep sea and the marine benthic realm, particularly with the development of multidisciplinary seafloor observatories. Since 2006, Ocean Networks Canada cabled observatories, has acquired nearly 65 TB and over 90,000 hours of video data from seafloor cameras and Remotely Operated Vehicles (ROVs). Manual processing of these data is time-consuming and highly labour-intensive, and cannot be comprehensively undertaken by individual researchers. These videos contain valuable information for faunal and environmental monitoring, and are a crucial source of information for assessing natural variability and ecosystem responses to increasing human activity in the deep sea. 2. In this study, we compared the performance of three groups of humans and one computer vision algorithm in counting individuals of the commercially important sablefish (or black cod) Anoplopoma fimbria, in recorded video from a cabled camera platform ...1. 近年来的技术进步提升了人类开展深海与海洋底栖生境研究的能力，尤其是多学科海底观测台站的发展为相关研究提供了关键支撑。自2006年以来，加拿大海洋网络（Ocean Networks Canada）的有线海底观测台站已通过海底摄像机与遥控水下机器人（Remotely Operated Vehicles, ROVs）采集了近65 TB的数据与超过90000小时的视频资料。对这些数据进行人工处理不仅耗时漫长、劳动强度极高，且单个研究人员无法完成全面的处理工作。这些视频蕴含着用于动物群落与环境监测的宝贵信息，同时也是评估深海自然环境变化、以及生态系统对人类活动加剧所做出响应的核心数据来源。 2. 本研究针对有线摄像机观测平台录制的视频素材，对比了三组人类受试者与一款计算机视觉算法在计数具有商业价值的裸盖鱼（sablefish，又名黑鳕，学名*Anoplopoma fimbria*）个体数量方面的性能表现……