# Robustness analysis of Deep Sky Objects detection models on HPC

arXiv: https://arxiv.org/abs/2508.09831 · HTML: https://mlsanity.com/paper/2508.09831/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [XRAI: Better Attributions Through Regions](https://mlsanity.com/paper/1906.02825/) (2019)
- [On the Robustness of Object Detection Models on Aerial Images](https://mlsanity.com/paper/2308.15378/) (2023)
- [Robustness of deep learning algorithms in astronomy -- galaxy morphology studies](https://mlsanity.com/paper/2111.00961/) (2021)

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

- [An Extended Evaluation Split for DeepSpaceYoloDataset](https://mlsanity.com/paper/2604.27593/) (2026)

## Abstract (arXiv)

Astronomical surveys and the growing involvement of amateur astronomers are producing more sky images than ever before, and this calls for automated processing methods that are accurate and robust. Detecting Deep Sky Objects -- such as galaxies, nebulae, and star clusters -- remains challenging because of their faint signals and complex backgrounds. Advances in Computer Vision and Deep Learning now make it possible to improve and automate this process. In this paper, we present the training and comparison of different detection models (YOLO, RET-DETR) on smart telescope images, using High-Performance Computing (HPC) to parallelise computations, in particular for robustness testing.