mlsanityData to 14 Oct 2026

D'etection d'objets c'elestes dans des images astronomiques par IA explicable

arXiv 2311.10592 ↗First posted 17 Nov 2023cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Amateur and professional astronomers can easily capture a large number of deep sky images with recent smart telescopes. However, afterwards verification is still required to check whether the celestial objects targeted are actually visible in the images produced. Depending on the magnitude of the targets, the observation conditions and the time during which the data is captured, it is possible that only stars are present in the images. In this study, we propose an approach based on explainable Artificial Intelligence to automatically detect the presence and position of captured objects. -- -- Grace `a l'apport des t'elescopes automatis'es grand public, les astronomes amateurs et professionnels peuvent capturer facilement une grande quantit'e d'images du ciel profond (comme par exemple les galaxies, n'ebuleuses, ou amas globulaires). N'eanmoins, une v'erification reste n'ecessaire `a post'eriori pour v'erifier si les objets c'elestes vis'es sont effectivement visibles dans les images produites: cela d'epend notamment de la magnitude des cibles, des conditions d'observation mais aussi de la dur'ee pendant laquelle les donn'ees sont captur'ees. Dans cette 'etude, nous proposons une approche bas'ee sur l'IA explicable pour d'etecter automatiquement la pr'esence et la position des objets captur'es.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 17 Nov 2023.

Builds on

5 of 22 references.

References · 5

  1. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  2. 2019Explainable Machine Learning for Scientific Insights and Discoveriescited by 166
  3. 2019XRAI: Better Attributions Through Regionscited by 116
  4. 2021From Kepler to Newton: Explainable AI for Sciencecited by 13
  5. 2023Landscape of High-performance Python to Develop Data Science and Machine Learning Applicationscited by 2

Built on by

Not cited yet.