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深海图像数据集用于生物检测

Takaki Nishio¹, Yuki Kawae¹

¹Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.

|February 2, 2026

概括

一个新的数据集,JODD,有助于深海生物的检测. 该资源支持开发用于海洋生物多样性研究和保护工作的自动化方法.

科学领域:

海洋生物学海洋生物学
计算机科学计算机科学
数据科学数据科学数据科学

背景情况:

有效的海洋资源保护和污染减缓取决于全面的深海生物多样性知识.
对生物分布的深海图像进行手动分析是耗时且不一致的,需要自动化解决方案.

研究的目的:

引入J-EDI生物检测数据集 (JODD),这是一个新型数据集,旨在训练和评估用于自动识别深海生物的机器学习模型.
为推进深海生态研究的自动化方法提供标准化资源.

主要方法:

汇编了8,151张图像,其中有15621个边界框,以"常见对象在上下文 (COCO) "格式进行注释.
包括在日本海洋地球科学与技术局 (JAMSTEC) 深海调查 (1984-2021) 期间通过远程操作车辆 (ROV) 和人驾驶车辆 (HOV) 捕获的图像.
数据集包括20个类别:19个生物组和1个机器类别,来源于JAMSTEC公开可用的深海图像电子图书馆 (J-EDI).

主要成果:

JODD数据集提供了大量多样化的注释深海图像集合.
数据集的结构是为了在开发和比较对象检测深度学习模型的直接应用.

结论:

JODD数据集对深海生态学和人工智能驱动的生物多样性评估领域做出了重大贡献.
这一资源将加速对海洋保护和研究至关重要的自动化工具的开发.

关键词:

人工智能的人工智能是人工智能.班托斯 (英语:Benthos) 是一种植物.深度学习是一种深度学习.这是一个HOV HOV.这是一个ROV ROV.在海底的海底.

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