# Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes

arXiv: https://arxiv.org/abs/2002.09211 · HTML: https://mlsanity.com/paper/2002.09211/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [PNet -- A Deep Learning Based Photometry and Astrometry Bayesian Framework](https://mlsanity.com/paper/2106.14349/) (2021)
- [Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers](https://mlsanity.com/paper/2211.05972/) (2022)
- [SSTD: Stripe-Like Space Target Detection Using Single-Point Weak Supervision](https://mlsanity.com/paper/2407.18097/) (2024)
- [Target Detection Framework for Lobster Eye X-Ray Telescopes with Machine Learning Algorithms](https://mlsanity.com/paper/2212.05497/) (2022)
- [High Performance Space Debris Tracking in Complex Skylight Backgrounds with a Large-Scale Dataset](https://mlsanity.com/paper/2506.02614/) (2025)
- [A Simulation-Augmented Benchmarking Framework for Automatic RSO Streak Detection in Single-Frame Space Images](https://mlsanity.com/paper/2305.00412/) (2023)
- [Direct initial orbit determination](https://mlsanity.com/paper/2308.14298/) (2023)
- [A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes](https://mlsanity.com/paper/2403.10206/) (2024)
- [An Image Quality Evaluation and Masking Algorithm Based On Pre-trained Deep Neural Networks](https://mlsanity.com/paper/2405.03408/) (2024)

## Abstract (arXiv)

Wide field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical targets detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as backbone network and a Feature Pyramid Network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has 2 times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25 % detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.