# Detecting streaks in smart telescopes images with Deep Learning

arXiv: https://arxiv.org/abs/2510.17540 · HTML: https://mlsanity.com/paper/2510.17540/

## Key facts

- Cited by 1 papers 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 (most cited first, 6 of 6)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Towards Total Recall in Industrial Anomaly Detection](https://mlsanity.com/paper/2106.08265/) (2021)
- [XRAI: Better Attributions Through Regions](https://mlsanity.com/paper/1906.02825/) (2019)
- [Landscape of High-performance Python to Develop Data Science and Machine Learning Applications](https://mlsanity.com/paper/2302.03307/) (2023)

## Built on by (most cited first, 1 of 1)

- [NEO-BENCH: A New Multi-Source Benchmark for Generalizable Astronomical Streak Detection](https://mlsanity.com/paper/2609.06774/) (2026)

## 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.