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一种用于自动检测潮流环境中表面流特征的实验方法

James Slingsby¹, Beth E Scott², Louise Kregting³

¹Environmental Research Institute, University of the Highlands and Islands, Thurso KW14 7EE, UK.

Sensors (Basel, Switzerland)

|October 16, 2024

概括

现在可以在无人机 (UAV) 图像中使用深度学习来自动检测水下流. 这项技术有助于评估海洋可再生能源对海洋巨型动物的影响.

科学领域:

海洋生物学海洋生物学
可再生能源工程可再生能源工程
计算机视觉计算机视觉计算机视觉

背景情况:

潮流环境对于海洋可再生能源 (MRE) 和作为海洋巨型动物的食场至关重要.
这些地区的水力动力学特征会影响猎物的可用性和巨型动物的行为,对MRE设备的相互作用构成风险.
无人驾驶飞行器 (UAV) 提供有关表面流和动物存在的高分辨率数据,这对于环境影响评估至关重要.

研究的目的:

开发和演示一种用于检测UAV图像中的表面流特征的自动化方法.
评估使用深度学习来分析潮流环境中的大型无人机数据集的可行性.

主要方法:

一个更快的R-CNN深度学习模型被用来自主检测kolk-boils (流特征).
该模型在预先标记的kolk-boils无人机图像上接受了训练,并使用环境条件特定的技术进行了增强.
评估了模型性能,其中75个时代的变种显示了最佳的回忆和精度.

主要成果:

该研究成功地展示了使用深度学习方法自动检测kolk-boils.
75个时代的Faster R-CNN模型实现了高平均回忆和精度.
局限性包括错误阳性检测的趋势,表明需要进一步改进的领域.

结论:

深度学习提供了一个可行的解决方案,用于自动检测来自潮流环境的无人机图像中的表面流.
需要进一步的研究来标准化数据,基准模型,并完善预处理技术以提高准确性.
这种自动化方法可以大大帮助人们了解和减轻MRE开发对环境的影响.

关键词:

深度学习是一种深度学习.环境监测环境监测环境监测机器学习是机器学习.海洋可再生能源海洋可再生能源远程传感是一种遥感技术.

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