Damage quantitative assessment of spacecraft in a large-size inspection

Kuo ZHANG; Jianliang HUO; Shengzhe WANG; Xiao ZHANG; Yiting FENG

doi:10.1631/FITEE.2000733

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Damage quantitative assessment of spacecraft in a large-size inspection

Special Feature on Application of Artificial Intelligence in Space Environment and Spacecraft | Updated：2022-05-19

- Damage quantitative assessment of spacecraft in a large-size inspection
  Enhanced Publication
- 大尺寸检查中航天器损伤定量评估
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 4, Pages: 542-554(2022)
- Affiliations：
  
  1.School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu611731, China
  2.Norla Institute of Technical Physics, Chengdu610041, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2000733
  CLC： TP391
- Received：30 December 2020，
  
  Accepted：29 September 2021，
  
  Published：2022-04
- Accepted：
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Kuo ZHANG, Jianliang HUO, Shengzhe WANG, et al. Damage quantitative assessment of spacecraft in a large-size inspection[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 542-554.
DOI：

Kuo ZHANG, Jianliang HUO, Shengzhe WANG, et al. Damage quantitative assessment of spacecraft in a large-size inspection[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 542-554. DOI： 10.1631/FITEE.2000733.

摘要

为保证航天器在多次航天任务中的安全性和可靠性，需要对航天器进行原位无损检测，判断微流星体和轨道碎片超高速撞击造成的损伤。本文提出一种创新的基于损伤重建图像拼接技术的定量损伤评估方法。首先，应用高斯混合模型聚类算法提取损伤特征突出的图像。然后，提出基于ORB特征提取算法和改进的具有自适应阈值选择的估计样本一致性（MSAC）算法的图像拼接方法，可创建用于损伤检测的大规模拼接图像。最后，对损伤特征区域进行分割和提取，生成拼接图像。通过计算质心位置和周长定量参数确定损伤区域的位置并判断损伤程度。实验结果验证了所提方法的有效性和适用性。

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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references

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