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arXiv 2609.38846cs.RO

珊瑚育成机器人评估系统(CGRAS):通过机器人和计算机视觉扩展珊瑚幼体监测规模

Coral Grow-out Robotic Assessment System (CGRAS): Scaling Coral Recruit Monitoring Through Robotics and Computer Vision

Dorian Tsai, Scarlett Raine, Emilio Olivastri, Riki Lamont, Andrew Lui, Timothy Morris, Joshua Esplin, Christopher A. Brunner, F. Mikaela Nordborg, Reginald War… 展开作者

Dorian Tsai, Scarlett Raine, Emilio Olivastri, Riki Lamont, Andrew Lui, Timothy Morris, Joshua Esplin, Christopher A. Brunner, F. Mikaela Nordborg, Reginald Wardleworth, Garima Samvedi, Karen Jackel, Matthew Dunbabin, Tobias Fischer, Andrea Severati

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中文总结 AI 辅助

针对珊瑚幼体监测瓶颈,提出结合机器人成像与计算机视觉的CGRAS系统,实现自动化多物种检测计数与健康评估,在大型养殖设施中降低9.6倍成本,计数一致性达96.4%。

中文摘要 AI 辅助

气候变化是对珊瑚礁的最大威胁,其日益加剧的全球影响加速了对可扩展的珊瑚礁修复技术的需求。大规模珊瑚礁修复依赖于珊瑚的批量生产,例如通过珊瑚水产养殖。使用在水产养殖设施中培育的珊瑚幼体进行珊瑚播种是一种可行的修复方法,但有效的生产需要对数万个宏观(直径0.5-2毫米)幼体进行持续、高频的监测,这使得传统的人工评估变得极其劳动密集。为了解决这一监测瓶颈,我们引入了珊瑚育成机器人评估系统(CGRAS),该系统结合机器人成像和计算机视觉,自动化数据采集,执行多物种的珊瑚检测和计数,并评估珊瑚健康状况。CGRAS自动提取珊瑚生长、存活和空间分布指标,旨在为操作员提供及时反馈,以优化生产、育成和部署工作流程。我们在一个大型水产养殖设施中,在标准化的珊瑚沉降瓦片上演示了CGRAS,与人工监测相比,时间和劳动力成本降低了9.6倍,同时对于Acropora kenti珊瑚,与专家计数的一致性达到96.4%。

英文摘要

Climate change is the largest threat to coral reefs, with increasing global impacts accelerating the need for scalable reef restoration technologies. Large-scale reef restoration depends on the mass production of corals, such as through coral aquaculture. Coral seeding with recruits grown in aquaculture facilities is a feasible restoration approach, but effective production requires consistent, high-frequency monitoring of tens of thousands of macroscopic (0.5-2mm diameter) recruits, making conventional manual assessment prohibitively labor-intensive. To address this monitoring bottleneck, we introduce the Coral Grow-out Robotic Assessment System (CGRAS) which combines robotic imaging and computer vision to automate data acquisition, perform multi-species detection and counting of corals, and evaluate coral health. CGRAS automatically extracts coral growth, survival and spatial distribution metrics, with the aim of providing timely feedback to operators for optimizing production, grow-out and deployment workflow processes. We demonstrate CGRAS in a large aquaculture facility on standardized coral settlement tiles, reducing the time and labor costs by a factor of 9.6 as compared to manual monitoring, whilst achieving 96.4% agreement for Acropora kenti corals relative to expert counts.

发表机构

  • Queensland University of Technology (QUT)(昆士兰科技大学)
  • Australian Institute of Marine Science (AIMS)(澳大利亚海洋科学研究所)

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