Effective framework for space target detection through atmospheric turbulence

Atmospheric turbulence is a major challenge in long-range imaging of ground-based telescopes, especially in the surveillance of space targets, whose observation distance is usually more than 100 km. In this case, space targets are extremely small in images, occupying less than 0.12% of the total image area, and suffer from severe blur and distortion. Consequently, the accuracy of object detection by both conventional and deep-learning-based methods is significantly hampered. Therefore, this paper proposes an effective framework for detecting space target through atmospheric turbulence. The framework incorporates a shallow deblurring module, a transformer-based feature extractor, and a small region proposal network. The training data comprises simulated degraded images of space target images against celestial backgrounds, as well as a selection of images from the Dotav2 dataset. Testing results show that the proposed framework outperforms the general framework, achieving a mean Average Precision (mAP) improvement of over 20%.