# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
###### 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. geostationary earth orbit (GEO); space object detection; PP-YOLOv2; linear fitting +
Footnote †: journal:  
under the extreme distance from the observer with weak target characteristics and complex star background. Several algorithms have already been developed for similar problems. [PERSON] et al. [5] proposed a RANSAC method based on statistical model and Monte Carlo algorithm which perform well at low SNR with moving thin overhead clouds for detecting tracks of slowly moving objects. [PERSON] et al. [6] proposed a pipeline named GP-ICP to detect the resident space objects in the GEO band. They took the strategy of detecting before tracking with the Gaussian process regression and a line-genuine technique. [PERSON] et al. [7] proposed a line-identifying technique combining a stacking method to detect uncatalogued GEO debris in CCD frames. [PERSON] et al. [8] introduced a multi-target detection algorithm for geostationary space objects based on topological sweep. They exploited the geometric duality to extract the target objects with an approximately linear trajectory. [PERSON] [9] introduced a robust algorithm called tracee for moving object detection, which will accelerate identifying tracklets by extracting linearly aligned points as a line segment from a three-dimensional space. To speed up the detection of space target, traditional algorithms based on hand-crafted feature selection need to be replaced by automatic artificial intelligence approaches. The literature highlights that deep learning (DL) models surpass all others in terms of accuracy and efficiency, making remarkable progress in various application scenarios including natural scenery images, remote sensing images and aerial images. Currently, convolutional neural network (CNN)-based object detection algorithms have strong advantages in terms of their superior performance in extract more abstract and robust high-level semantic information, along with high plasticity and universality. It is equally important that dataset play a dominant role in shaping the future of DL models as DL itself is a data-driven approach. Consequently, we stand to seize the opportunity and momentum to take further research on the object detection problem in deep space scene with star background based on the SpotGEO dataset. In this paper, a multi-frame optical detection algorithm for GEO target is proposed on the strength of the imaging characteristics and motion information. Motivated by the state-of-the-art object detection method for natural scene imagery, we conduct a real-time DL framework called PP-YOLOv2 [10] for single-frame object detection and design a post  
Figure 1: A diagram of a ground-based space object surveillance platform.