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Updated: Feb 19, 2026

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

珊瑚-CRCA:一种颜色参考图自动化算法用于珊瑚漂白可视化和严重性评估.

Mahmoud Elmezain¹, Atif Sultan¹, Mobeen Ur Rehman²

¹Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, United Arab Emirates.

Marine pollution bulletin

|February 17, 2026

概括

这项研究引入了一种自动化的AI系统,用于使用水下图像和彩色图表进行珊瑚漂白评估. 珊瑚-CRCA算法准确量化了漂白水平,有助于监测海洋生态系统.

关键词:

珊瑚的AI和计算机视觉珊瑚漂白分析的分析对珊瑚进行监测.珊瑚礁中的珊瑚礁在 CoralWatch 的自动化系统中,生态保护生态保护在水下计算机视觉.

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Published on: June 13, 2025

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科学领域:

海洋生物学海洋生物学
生态系统监测生态系统监测
人工智能的人工智能

背景情况:

珊瑚礁是重要的海洋生态系统,受到海温上升和污染的威胁,导致广泛的漂白.
目前的监控方法是劳动密集型的,并与水下图像质量作斗争.
现有的AI方法缺乏细粒度的本地化和完全的自动化.

研究的目的:

利用水下图像和颜色参考图表开发一个完全自动化的珊瑚漂白评估算法.
提高珊瑚礁健康监测的准确性和效率.
为了解决当前方法中手动注释和图像噪声的局限性.

主要方法:

建议的珊瑚颜色参考图表自动化 (珊瑚-CRCA),一个多阶段的算法.
实现了用于水下扭曲的图像拒绝.
自动珊瑚细分,图表分析和使用颜色相似性的像素级漂白评估.

主要成果:

在漂白百分比估计中达到19.17%的平均绝对误差.
达到了96.12%的二元分类准确度 (漂白/健康).
在从阿拉伯/波斯湾收集的3400张现场图像上展示了专家级别的性能.

结论:

珊瑚-CRCA算法成功自动化珊瑚漂白评估,匹配专家的性能.
这种人工智能驱动的方法提高了监测退化的珊瑚礁的稳定性和准确性.
开发的系统为全球评估珊瑚健康提供了一个可扩展的解决方案.