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Updated: Jun 4, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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YOLO-Dynamic: A Detection Algorithm for Spaceborne Dynamic Objects.

Haiying Zhang1, Zhengyang Li2, Chunyan Wang1

  • 1Opto-Electronics Engineering College, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces YOLO-Dynamic, an advanced algorithm for detecting space objects like asteroids and debris. It enhances small-object recognition and computational efficiency for safer space operations.

Keywords:
LASF_NeckSC_Block_C2fYOLOv8multi-scale feature fusionspaceborne dynamic object detection

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Area of Science:

  • Space situational awareness
  • Computer vision
  • Astrodynamics

Background:

  • Accurate detection of spaceborne objects is critical for space safety.
  • Existing models struggle with complex environments and small object detection.

Purpose of the Study:

  • Introduce YOLO-Dynamic, a novel algorithm for enhanced spaceborne object detection.
  • Improve detection accuracy and computational efficiency.

Main Methods:

  • Developed SC_Block_C2f module integrating StarNet and Convolutional Gated Linear Unit (CGLU) for improved feature extraction.
  • Designed LASF_Neck with a lightweight multi-scale architecture for optimized feature fusion.
  • Validated performance on real-world images from Antarctic observatories.

Main Results:

  • YOLO-Dynamic achieved a 7% increase in mAP@0.5 and 10.3% improvement in mAP@0.5:0.95 compared to YOLOv8s.
  • Reduced parameters by 1.48 M and floating-point operations by 3.8 G.
  • Demonstrated superior detection accuracy and computational efficiency.

Conclusions:

  • YOLO-Dynamic offers significant improvements in detecting spaceborne dynamic objects.
  • The algorithm is computationally efficient, suitable for real-world applications.
  • Enhances safety and reliability in space operations.