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http://dx.doi.org/10.5139/JKSAS.2017.45.9.794

A Study on the Deep Neural Network based Recognition Model for Space Debris Vision Tracking System  

Lim, Seongmin (Department of Aerospace System Engineering, Korea University of Science & Technology)
Kim, Jin-Hyung (IT Convergence Technology Team, Korea Aerospace Research Institute)
Choi, Won-Sub (IT Convergence Technology Team, Korea Aerospace Research Institute)
Kim, Hae-Dong (IT Convergence Technology Team, Korea Aerospace Research Institute)
Publication Information
Journal of the Korean Society for Aeronautical & Space Sciences / v.45, no.9, 2017 , pp. 794-806 More about this Journal
Abstract
It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.
Keywords
Space Debris; Active Debris Removal; Deep Neural Network; Feature Encoding; Model Ensemble;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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