CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-09-29
Cited: 0
Clicked: 3598
Citations: Bibtex RefMan EndNote GB/T7714
Kuo ZHANG, Jianliang HUO, Shengzhe WANG, Xiao ZHANG, Yiting FENG. Damage quantitative assessment of spacecraft in a large-size inspection[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 542-554.
@article{title="Damage quantitative assessment of spacecraft in a large-size inspection",
author="Kuo ZHANG, Jianliang HUO, Shengzhe WANG, Xiao ZHANG, Yiting FENG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="4",
pages="542-554",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000733"
}
%0 Journal Article
%T Damage quantitative assessment of spacecraft in a large-size inspection
%A Kuo ZHANG
%A Jianliang HUO
%A Shengzhe WANG
%A Xiao ZHANG
%A Yiting FENG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 4
%P 542-554
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000733
TY - JOUR
T1 - Damage quantitative assessment of spacecraft in a large-size inspection
A1 - Kuo ZHANG
A1 - Jianliang HUO
A1 - Shengzhe WANG
A1 - Xiao ZHANG
A1 - Yiting FENG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 4
SP - 542
EP - 554
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000733
Abstract: To ensure the safety and reliability of spacecraft during multiple space missions, it is necessary to conduct in-situ nondestructive detection of the spacecraft to judge the damage caused by the hypervelocity impact of micrometeoroids and orbital debris (MMOD). In this paper, we propose an innovative quantitative assessment method based on damage reconstructed image mosaic technology. First, a Gaussian mixture model clustering algorithm is applied to extract images that highlight damage characteristics. Then, a mosaicking scheme based on the ORB feature extraction algorithm and an improved M-estimator SAmple Consensus (MSAC) algorithm with an adaptive threshold selection method is proposed which can create large-scale mosaicked images for damage detection. Eventually, to create the mosaicked images, the damage characteristic regions are segmented and extracted. The location of the damage area is determined and the degree of damage is judged by calculating the centroid position and the perimeter quantitative parameters. The efficiency and applicability of the proposed method are verified by the experimental results.
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