Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
Yuqi Dai, Tie Zheng, Changbin Xue, Li Zhou
Abstract
The limited resource of Geostationary Earth Orbit (GEO) is precious and most telecommunication, weather and navigational satellites are placed in this orbit. In order to guarantee the safety and health of active satellites, advanced surveillance and warning of unknown space targets such as space debris are crucial. However, space object detection still remains a very challenging problem because of the weak target characteristics and complex star background. To solve this problem, we conduct a deep-learning-based framework called PP-YOLOv2 for single-frame object detection and design a post-processing algorithm named CFS for further candidate filtration and supplement. First, we transform the label information and generate the according bounding boxes to train the PP-YOLOv2 detector to extract candidate coordinates for each frame. Then, the CFS technique is designed as an effective post-processing procedure to obtain the eventual prediction results. Experiments were conducted over a dataset from the Kelvins SpotGEO challenge, which demonstrate the effectiveness and the comparable detection performance of our proposed pipeline. Finally, the deployment results on NVIDIA Jetson Nano show that the proposed method has a competitive application prospect for a space target monitoring system.