StreakMind: AI detection and analysis of satellite streaks in astronomical images with automated database integration

2026-05-05

AI Summary

This work addresses the significant contamination of astronomical survey images by streaks from artificial satellites and space debris, which severely degrades data quality. The authors propose the first end-to-end automated framework for detecting and characterizing such streaks, introducing the novel application of the YOLO OBB model to astronomical streak detection. The pipeline integrates geometric refinement, cross-frame association, Gaussian-based confidence scoring, and cross-validation against orbital databases to enable fully automated processing—from image detection to structured archival. Evaluated on a test dataset, the system achieves 94% precision and 97% recall, demonstrating robust detection of faint streaks alongside high-fidelity geometric reconstruction and reliable satellite identification.

Abstract

Artificial satellites and space debris increasingly contaminate astronomical images, affecting scientific surveys and producing large volumes of streaked exposures. Manual inspection is no longer feasible at scale, and reliable detection and characterisation of streaks has become essential for both data-quality control and the monitoring of objects in Earth orbit. We present StreakMind, an automated pipeline designed to detect Near-Earth Objects and satellite streaks in astronomical images, characterise their geometry, and cross-identify them with known orbital objects. The system integrates all inference results into a structured database suitable for large surveys. A YOLO OBB model was trained on a hybrid dataset of 2335 images and applied to processed FITS frames. Geometric refinement, inter-frame association, satellite cross-identification, and Gaussian-based confidence scoring were then used to produce final identifications stored in a relational database. Observations from La Sagra Observatory were used to develop and test the method. On the test set, the model achieved a precision of 94 percent and a recall of 97 percent. It reliably detected faint streaks, delivered consistent geometric reconstructions, and performed robust satellite cross-identification. StreakMind demonstrates strong potential for large-scale automated analysis of linear streaks produced by both Near-Earth Objects and artificial satellites, contributing to space situational awareness.

Problem

• satellite streaks
• astronomical images
• space debris
• data-quality control
• space situational awareness

Innovation

• satellite streak detection
• YOLO OBB
• automated database integration
• space situational awareness
• astronomical image processing