mlsanityData to 14 Oct 2026

Detecting streaks in smart telescopes images with Deep Learning

arXiv 2510.17540 ↗First posted 20 Oct 2025astro-ph.IM cs.CVcited by 1

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Abstract (arXiv)

The growing negative impact of the visibility of satellites in the night sky is influencing the practice of astronomy and astrophotograph, both at the amateur and professional levels. The presence of these satellites has the effect of introducing streaks into the images captured during astronomical observation, requiring the application of additional post processing to mitigate the undesirable impact, whether for data loss or cosmetic reasons. In this paper, we show how we test and adapt various Deep Learning approaches to detect streaks in raw astronomical data captured between March 2022 and February 2023 with smart telescopes.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 42 references that are in the map. First posted on arXiv 20 Oct 2025.

Builds on

6 of 42 references.

References · 6

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  2. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  3. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  4. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  5. 2019XRAI: Better Attributions Through Regionscited by 116
  6. 2023Landscape of High-performance Python to Develop Data Science and Machine Learning Applicationscited by 2

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