We introduce a machine learning-based method for extracting HI sources from
3D spectral data, and construct a dedicated dataset of HI sources from CRAFTS.
Our custom dataset provides comprehensive resources for HI source detection.
Utilizing the 3D-Unet segmentation architecture, our method reliably identifies
and segments HI sources, achieving notable performance metrics with recall
rates reaching 91.6% and accuracy levels at 95.7%. These outcomes substantiate
the value of our custom dataset and the efficacy of our proposed network in
identifying HI source. Our code is publicly available at
this https URL

介绍了一种基于机器学习的方法用于从三维光谱数据中提取 HI 源，并构建了一个专用的 HI 源数据集。利用 3D-Unet 分割架构，我们的方法可靠地识别和分割 HI 源，达到了 91.6% 的召回率和 95.7% 的准确性水平。这些结果证实了我们定制数据集的价值以及我们提出的网络在识别 HI 源方面的功效。我们的代码公开在该网址上。

使用深度学习方法自动识别和分割 CRAFTS 中的高连接处信息

Automated Identification and Segmentation of Hi Sources in CRAFTS Using  Deep Learning Method

Accurately quantifying and removing submerged underwater waste plays a
crucial role in safeguarding marine life and preserving the environment. While
detecting floating and surface debris is relatively straightforward,
quantifying submerged waste presents significant challenges due to factors like
light refraction, absorption, suspended particles, and color distortion. This
paper addresses these challenges by proposing the development of a custom
dataset and an efficient detection approach for submerged marine debris. The
dataset encompasses diverse underwater environments and incorporates
annotations for precise labeling of debris instances. Ultimately, the primary
objective of this custom dataset is to enhance the diversity of litter
instances and improve their detection accuracy in deep submerged environments
by leveraging state-of-the-art deep learning architectures.

本文提出了一个基于深度学习的高效检测方法和包含多种水下环境的标注数据集，以更精确地定位、分类和移除水下废弃物及海底垃圾，并解决由于光线折射、颜色失真等因素带来的诸多挑战。