Rocket booster separation motion parameter measurement method based on deep learning

Abstract

The invention relates to a rocket booster separation motion parameter measurement method based on deep learning. The method comprises the following steps: carrying out normalization and standardization preprocessing on received image or video data; inputting the preprocessed data into a feature extraction network, and performing down-sampling on a picture to obtain a feature map; respectively carrying out regression on the central point, the scale, the depth and the attitude by utilizing a decoding network consisting of a convolutional layer and an activation function to obtain output; calculating the three-dimensional position of the center of the rocket booster; if the received data is a video or a sequential image sequence of the same video, smoothing the predicted three-dimensional position and posture; and calculating to obtain the speed and the angular speed when the rocket booster is separated. According to the method, the obtained data is input into the network model, an end-to-end single-step pose measurement algorithm is realized, post-processing such as non-maximum suppression is not needed, the operation speed of the algorithm is improved, and then motion parameters of rocket booster separation are obtained through calculation. Compared with the prior art, the method does not need specific geometric features during pose measurement, needs fewer known conditions, and has the advantages of high processing speed, high precision and high robustness in a complex environment.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a method for measuring separation motion parameters of a rocket booster based on deep learning. Background technique [0002] When the rocket is launched, the rocket booster provides power to the rocket to achieve a predetermined speed, and then quickly separates from the rocket body. Accurately measuring the motion parameters of the booster after separation can provide data support for the prediction and recovery of the landing position of the booster. It can also be used to judge whether the separation was successful. Vision-based motion parameter measurement has the characteristics of no contact with the measured object and high measurement accuracy, and has been widely used in public medical, aerospace, automotive electronics and other fields. The measurement of target pose parameters is the core content of vision-based motion parameter measurement, and is ...

AI Technical Summary

Problems solved by technology

Secondly, when the rocket and booster are separated, fire, smoke, etc. will be generated, and the image quality of the camera will change accordingly, which will affect the measurement accuracy

Third, complex backgrounds can also greatly interfere with geometric feature-based methods

However, the attitude of the rocket booster requires three degrees of freedom to describe, and the 3D object detection network cannot complete the task

Images