In the satellite pose estimation problem, the deep learning method is used to train the network. The satellite pose needs to estimate the rotation (R) and translation (T), which are difficult to be well estimated simultaneously due to the internal coupling interaction. To solve the above problems, a dual-channel satellite pose estimation network based on ResNet50 is proposed to decouple the rotation and translation of satellite, effectively avoid the interaction, and estimate the translation and rotation of satellite respectively through the constructed network, which improves the recognition effect of satellite attitude. Through experimental verification, the network model constructed in this paper has better effect on the estimation of rotation and translation compared with other methods.
To accurately identify the dynamically changing water level in the compartment, Firstly, it is needed to pre-process the image by image grayscale, image segmentation, morphological processing, apply PP-YOLO v2 algorithm to measure the water level with dynamic changing characteristics, simulate the compartment through the water tank, convert the actual level according to the proportion, and finally compare the algorithm detection result with the actual measurement result by experiment. Finally, the algorithm detection results are compared with the actual measurement results through experiments, and the relative error is derived. The experimental results show that the dynamic water level identification algorithm of compartment with PP-YOLO v2 has high accuracy, and the relative error is only 1.33%. The accuracy of this algorithm for dynamic change of water level recognition is 98.67%, which has strong application value.
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