Journal of Astronautics ›› 2021, Vol. 42 ›› Issue (10): 1283-1292.doi: 10.3873/j.issn.1000-1328.2021.10.009

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Geostationary Orbit Object Detection Based on Deep Learning

HUANG Xi yao, HE Yi ting, DU Hua jun, ZENG Xiang yuan, LIU Tian ci, SHAN Wen jing, CHENG Lin   

  1. 1. School of Automation, Beijing Institute of Technology, Beijing 100081, China;2. National Key Laboratory of Science and Technology on Aerospace Intelligent Control,Beijing Aerospace Automatic Control Institute, Beijing 100854, China;3. School of Astronautics, Beihang University, Beijing 102206, China
  • Received:2020-10-09 Revised:2021-03-25 Online:2021-10-15 Published:2021-10-15

Abstract: A deep learning based method is proposed to detect GEO objects from the low precision CCD images for the ESA “SpotGEO” competition. The Gaussian process regression and template matching method are adopted in the image data preprocessing step. According to the motion characteristics of GEO objects, the topological sweeping method is used as a preliminary step. To reduce the noise effect, an object filtering method is proposed. Two additional data filters are set before and after the topological sweeping respectively using the convolutional neural network. They significantly decrease the number of noise points and increase the detection accuracy. Results show that this method can reach a high detection accuracy of 98%, which is suitable for the sophisticated environment with light pollution and clouds covering.

Key words: Space object detection, Geostationary orbit, Deep learning, Topological sweeping

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