Robustness analysis of Deep Sky Objects detection models on HPC
This work addresses the need for accurate and robust automated detection methods for astronomers and researchers handling large volumes of sky images, but it appears incremental as it applies existing models to a specific domain.
The paper tackled the challenge of detecting Deep Sky Objects in astronomical images by training and comparing YOLO and RET-DETR models on smart telescope data, using High-Performance Computing to parallelize computations for robustness testing.
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.