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An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation release_l53u5sknuvfodexzuryleqj6zm

by Junjie Xu, Bin Song, Xi Yang, Xiaoting Nan

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abstracts[] {'sha1': 'a31d11e43c680ba37ffe375d0867a980c120f7cb', 'content': 'The on-board pose estimation of uncooperative target is an essential ability for close-proximity formation flying missions, on-orbit servicing, active debris removal and space exploration. However, the main issues of this research are: first, traditional pose determination algorithms result in a semantic gap and poor generalization abilities. Second, specific pose information cannot be accurately known in a complicated space target imaging environment. Deep learning methods can effectively solve these problems; thus, we propose a pose estimation algorithm that is based on deep learning. We use keypoints detection method to estimate the pose of space targets. For complicated space target imaging environment, we combined the high-resolution network with dilated convolution and online hard keypoint mining strategy. The improved network pays more attention to the obscured keypoints, has a larger receptive field, and improves the detection accuracy. Extensive experiments have been conducted and the results demonstrate that the proposed algorithms can effectively reduce the error rate of pose estimation and, compared with the related pose estimation methods, our proposed model has a higher detection accuracy and a lower pose determination error rate in the speed dataset.', 'mimetype': 'application/xml+jats', 'lang': None}
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issue 23
language en
license_slug CC-BY
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original_title
pages 3857
publisher MDPI AG
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release_date 2020-11-25
release_stage published
release_type article-journal
release_year 2020
subtitle
title An Improved Deep Keypoint Detection Network for Space Targets Pose Estimation
version
volume 12
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As JSON via API

Extra Metadata (raw JSON)

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crossref.subject ['General Earth and Planetary Sciences']
crossref.type journal-article