Coral reefs are fast-changing and complex ecosystems that are crucial to monitor and study. Biological hotspot detection can help coral reef managers prioritize limited resources for monitoring and intervention tasks. Here, we explore the use of autonomous underwater vehicles (AUVs) with cameras, coupled with visual detectors and photogrammetry, to map and identify these hotspots. This approach can provide high spatial resolution information in fast feedback cycles. To the best of our knowledge, we present one of the first attempts at using an AUV to gather visually-observed, fine-grain biological hotspot maps in concert with topography of a coral reefs. Our hotspot maps correlate with rugosity, an established proxy metric for coral reef biodiversity and abundance, as well as with our visual inspections of the 3D reconstruction. We also investigate issues of scaling this approach when applied to new reefs by using these visual detectors pre-trained on large public datasets.

翻译：珊瑚礁是快速变化且复杂的生态系统，对其进行监测和研究至关重要。生物热点检测可帮助珊瑚礁管理者优先分配有限资源，用于监测和干预任务。本文探索了使用搭载摄像机的自主水下机器人（AUV），结合视觉检测器与摄影测量技术，来测绘并识别这些热点区域。该方法能够以快速反馈周期提供高空间分辨率信息。据我们所知，本文首次尝试利用AUV结合珊瑚礁地形，收集基于视觉观测的细粒度生物热点图。我们的热点图与粗糙度（珊瑚礁生物多样性和丰度的公认代理指标）以及3D重建的视觉检查结果具有相关性。此外，我们探讨了将该方法应用于新礁区时的扩展性问题，通过使用在大规模公共数据集上预训练的视觉检测器实现。