mlsanityData to 7 Oct 2026

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

arXiv 2303.04506 ↗First posted 8 Mar 2023cs.CVcited by 4

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Abstract (arXiv)

In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is to provide an overview of existing techniques, in terms of prediction performance and computational efficiency, to scientists in the astrophysics community who would like to employ machine learning in their research.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 53 of its 108 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

53 of 108 references.

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Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 1.

4 papers.

  1. 2024CosmoCLIP: Generalizing Large Vision-Language Models for Astronomical Imagingcited by 4
  2. 2023RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generationcited by 2
  3. 2024XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Imagescited by 0
  4. 2025StrCGAN: A Generative Framework for Stellar Image Restorationcited by 0