Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning Method
<|reference_start|>Automated Identification and Segmentation of Hi Sources in CRAFTS Using Deep Learning Method: 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 https://github.com/fishszh/HISF.<|reference_end|>
@article{song2024automated,
    title={Automated Identification and Segmentation of Hi Sources in CRAFTS Using
  Deep Learning Method},
    author={Zihao Song, Huaxi Chen, Donghui Quan, Di Li, Yinghui Zheng, Shulei Ni,
  Yunchuan Chen, Yun Zheng},
    journal={arXiv preprint arXiv:2403.19912},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19912},
    primaryClass={cs.CV astro-ph.GA astro-ph.IM}
}