View in EDS

LDFNN: A lightweight dual-feedback neural network for space target component detection.

Saved in:
Bibliographic Details
Title: LDFNN: A lightweight dual-feedback neural network for space target component detection.
Authors: Zhou, Wenbo1,2 (AUTHOR) zhouwenbo20@mails.ucas.ac.cn, Wang, Zebin3 (AUTHOR) 961864171@qq.com, Li, Ligang1,4 (AUTHOR) liligang@nssc.ac.cn, Liu, Bo1,5 (AUTHOR) liubo24@sdufe.edu.cn, Wu, Yijia1,2 (AUTHOR) wuyijia23@mails.ucas.ac.cn
Source: Advances in Space Research. Apr2025, Vol. 75 Issue 7, p5702-5717. 16p.
Subjects: Image quality analysis, Transfer functions, Computational complexity, Orbits (Astronomy), Space vehicles
Abstract: As the number of spacecraft and the complexity of their missions increase, space target component detection technology is becoming critical for ensuring spacecraft safety in orbit. An effective detection system not only monitors and identifies potential collision threats in real-time, enabling timely obstacle avoidance or path adjustments, but also provides crucial localization and identification capabilities for autonomous spacecraft maintenance and repair. However, most existing methods overlook image quality degradation resulting from prolonged on-orbit operation. Although deep learning models have improved detection accuracy, they are often burdened by excessive network parameters and high computational complexity, which limits their applicability to low-power, lightweight, and resource-constrained on-board processing. In this study, we propose a lightweight dual-feedback neural network (LDFNN) for space target component detection and recognition. The network is designed to address the challenges posed by the space environment through a dual-feedback mechanism. The outer-feedback mechanism enhances recognition stability and accuracy by incorporating modulation transfer function (MTF) analysis and image quality assessment, while the inner-feedback mechanism is optimized for low-power on-orbit processing, improving both real-time performance and generalization. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:As the number of spacecraft and the complexity of their missions increase, space target component detection technology is becoming critical for ensuring spacecraft safety in orbit. An effective detection system not only monitors and identifies potential collision threats in real-time, enabling timely obstacle avoidance or path adjustments, but also provides crucial localization and identification capabilities for autonomous spacecraft maintenance and repair. However, most existing methods overlook image quality degradation resulting from prolonged on-orbit operation. Although deep learning models have improved detection accuracy, they are often burdened by excessive network parameters and high computational complexity, which limits their applicability to low-power, lightweight, and resource-constrained on-board processing. In this study, we propose a lightweight dual-feedback neural network (LDFNN) for space target component detection and recognition. The network is designed to address the challenges posed by the space environment through a dual-feedback mechanism. The outer-feedback mechanism enhances recognition stability and accuracy by incorporating modulation transfer function (MTF) analysis and image quality assessment, while the inner-feedback mechanism is optimized for low-power on-orbit processing, improving both real-time performance and generalization. [ABSTRACT FROM AUTHOR]
ISSN:02731177
DOI:10.1016/j.asr.2025.01.052