Spacecraft defect detection method based on LVQ-GMM algorithm and multi-objective optimization segmentation algorithm

Abstract

The invention discloses a spacecraft defect detection method based on an LVQ-GMM algorithm and a multi-objective optimization segmentation algorithm. According to the spacecraft defect detection method based on LVQ-GMM and multi-objective optimization segmentation, column-direction search comparison is carried out through the maximum temperature point value in infrared thermal image sequence datato obtain a transformation column step length; meanwhile, the data is partitioned by utilizing the maximum temperature value in the transient thermal response curve; obtaining a transformation row step length of each data block; according to the method, sampling is carried out by using a transformation column step length and a transformation row step length to obtain a sampling data set composed of transient thermal response curves containing typical temperature changes, and a Gaussian mixture model corresponding to classification of the sampling data set is obtained by using an LVQ-GMM algorithm, so that the corresponding probability of the classification data set is obtained. And classifying each transient thermal response curve in the data set by using the probability, and reconstructing a defect image by using the classified typical thermal response curve. And constructing a double-layer multi-target optimized thermal image segmentation framework to realize accurate segmentation ofdefects.

Description

technical field [0001] The invention belongs to the field of space debris impact damage detection application and pattern recognition technology of spacecraft, more specifically, the invention relates to a spacecraft defect detection method based on LVQ-GMM algorithm and multi-objective optimal segmentation algorithm. Background technique [0002] The deteriorating space debris environment poses a serious threat to spacecraft, especially a large number of millimeter and micron-sized tiny space debris such as tiny meteoroids and orbital debris, whose impact speed reaches several kilometers per second or even tens of kilometers per second, and cannot be dealt with It performs tracking, early warning and maneuver avoidance, which has a very high probability of collision with the spacecraft, and the damage is quite huge. Long-term in-orbit spacecraft (such as space shuttles, communication satellites, international space stations, etc.) have been impacted by tiny space debris to ...

AI Technical Summary

Problems solved by technology

On the one hand, if the defect detection rate is improved to a certain extent under the premise of fully satisfying the preservation of details, the noise is also retained, which is easy to cause misjudgment of defect recognition, resulting in an increase in the false detection rate

On the other hand, if only the overall denoising of the image is satisfied, the damage defects caused by the impact of tiny space debris are small in size and large in number, and these tiny defects similar to the noise will be removed together with the denoising process, reducing the defect The detection rate and detection accuracy

Therefore, the above-mentioned conventional segmentation method is applied to the object of the present invention, that is, the infrared reconstructed image of the defect. Since the false detection rate and detection rate of the defect cannot be weighed, the segmentation effect is not satisfactory.

Especially considering that the infrared thermal image reflects the thermal radiation information of the test piece, which is easily affected by the environment, imaging links, etc., so that the background noise of the obtained infrared reconstruction image of defects is large.

At the same time, due to the difference in surface heat radiation ability between the defect area and the background area, the edge of the infrared reconstruction image of the defect is not smooth enough, which is not conducive to image segmentation

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