Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Space Object Detection using Multi-frame Temporal Trajectory Completion Method

Xiaoqing Lan, Biqiao Xin, Bingshu Wang, Han Zhang, Rui Zhu, Laixian Zhang

Abstract

Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this, we propose a multi-frame temporal trajectory completion scheme centered on the Hungarian algorithm for globally optimal cross-frame matching. To effectively mitigate missing and false detections, a series of key steps including temporal matching and interpolation completion, temporal-consistency-based noise filtering, and progressive trajectory refinement are designed in the post-processing pipeline. Experimental results on the public SpotGEO dataset demonstrate the effectiveness of the proposed method, achieving an F_1 score of 90.14%.

Space Object Detection using Multi-frame Temporal Trajectory Completion Method

Abstract

Space objects in Geostationary Earth Orbit (GEO) present significant detection challenges in optical imaging due to weak signals, complex stellar backgrounds, and environmental interference. In this paper, we enhance high-frequency features of GEO targets while suppressing background noise at the single-frame level through wavelet transform. Building on this, we propose a multi-frame temporal trajectory completion scheme centered on the Hungarian algorithm for globally optimal cross-frame matching. To effectively mitigate missing and false detections, a series of key steps including temporal matching and interpolation completion, temporal-consistency-based noise filtering, and progressive trajectory refinement are designed in the post-processing pipeline. Experimental results on the public SpotGEO dataset demonstrate the effectiveness of the proposed method, achieving an F_1 score of 90.14%.
Paper Structure (10 sections, 4 equations, 3 figures, 2 tables)

This paper contains 10 sections, 4 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. METHODOLOGY
  3. Transformation of Dataset Labels
  4. Single Frame Detection Based on Wavelet-Transform Algorithm
  5. Trajectory Processing Based on Multi-frame Temporal Completion Algorithm
  6. EXPERIMENTS
  7. Experimental Setup
  8. Quantitative Analysis
  9. Visual Results
  10. CONCLUSION

Figures (3)

  • Figure 1: The framework of the proposed method. The WTNet module is used to perform initial single-frame target detection. The multi-frame temporal trajectory completion method centered on the Hungarian algorithm is applied to filter and supplement the initial detection results.
  • Figure 2: Visual results of sequential detection. The left column are original observation frames; the middle column are single-frame detection results; the right column are the final detection results after multi-frame temporal trajectory completion. The blue dots denote ground truth, the green dots represent true positives, the yellow dots indicate false positives, and red dots correspond to false negatives.
  • Figure 3: Single-frame detection results of the proposed method on the BUAA-MSOD dataset. The first column shows the original images; the second column presents the ground truth (GT) labels; the third and fourth columns display the detection results before and after model lightweighting, respectively. Blue, green, yellow, and red points represent real targets, true positives, false positives, and false negatives, respectively.