基于双层多目标分割的超高速撞击航天器损伤红外检测算法

杨晓; 殷春; Sara DADRAS; 雷光钰; 谭旭彤; 邱根

doi:10.1631/FITEE.2000695

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基于双层多目标分割的超高速撞击航天器损伤红外检测算法

人工智能在空间环境和航天器中的应用专题 | Updated：2022-05-19

- 基于双层多目标分割的超高速撞击航天器损伤红外检测算法
  Enhanced Publication
- Spacecraft damage infrared detection algorithm for hypervelocity impact based on double-layer multi-target segmentation
- 信息与电子工程前沿（英文） 2022年23卷第4期页码：571-586
- Affiliations：
  
  1.School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu611731, China
  2.Electrical and Computer Engineering Department, Utah State University, UT84321, USA
- Author bio：
  
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(61873305);Applied Basic Research Program of Sichuan Province, China(2018JY0410;2019YJ0199)
- DOI：10.1631/FITEE.2000695
  中图分类号： TP391.4
- 收稿：2020-12-12，
  
  录用：2021-06-28，
  
  纸质出版：2022-04
- Accepted：
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杨晓, 殷春, Sara DADRAS, 等. 基于双层多目标分割的超高速撞击航天器损伤红外检测算法[J]. 信息与电子工程前沿（英文）, 2022,23(4):571-586.

Xiao YANG, Chun YIN, Sara DADRAS, et al. Spacecraft damage infrared detection algorithm for hypervelocity impact based on double-layer multi-target segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 571-586.
杨晓, 殷春, Sara DADRAS, 等. 基于双层多目标分割的超高速撞击航天器损伤红外检测算法[J]. 信息与电子工程前沿（英文）, 2022,23(4):571-586. DOI： 10.1631/FITEE.2000695.

Xiao YANG, Chun YIN, Sara DADRAS, et al. Spacecraft damage infrared detection algorithm for hypervelocity impact based on double-layer multi-target segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(4): 571-586. DOI： 10.1631/FITEE.2000695.

摘要

针对超高速撞击引起的航天器损伤检测，提出一种先进的基于红外成像检测的航天器缺陷提取算法。采用高速混合模型对红外视频流采样数据中的温度变化特征进行分类，并重构图像，得到反映缺陷特征的红外重构图像。设计的分割目标函数用于保证图像分割结果对噪声去除和细节保留的有效性，同时考虑到红外重构图像的复杂性，即所需权衡不同。因此，引入多目标优化算法以实现细节保留和噪声去除之间的平衡，并采用基于分解的多目标进化算法（MOEA/D）进行优化，以保证损伤分割的准确性。实验结果验证了所提算法的有效性。

Abstract

To detect spacecraft damage caused by hypervelocity impact

we propose an advanced spacecraft defect extraction algorithm based on infrared imaging detection. The Gaussian mixture model (GMM) is used to classify the temperature change characteristics in the sampled data of the infrared video stream and reconstruct the image to obtain the infrared reconstructed image (IRRI) reflecting the defect characteristics. The designed segmentation objective function is used to ensure the effectiveness of image segmentation results for noise removal and detail preservation

while taking into account the complexity of IRRI (that is

the required trade-offs are different). A multi-objective optimization algorithm is introduced to achieve balance between detail preservation and noise removal

and a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is used for optimization to ensure damage segmentation accuracy. Experimental results verify the effectiveness of the proposed algorithm.

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