[1]LI Linze,ZHANG Tao.Feature detection and recognition of spatial noncooperative objects based on deep learning[J].CAAI Transactions on Intelligent Systems,2020,15(6):1154-1162.[doi:10.11992/tis.202006011]
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Feature detection and recognition of spatial noncooperative objects based on deep learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 6 Page number: 1154-1162 Column: 学术论文—机器感知与模式识别 Public date: 2020-11-05
Title:
Feature detection and recognition of spatial noncooperative objects based on deep learning
Author(s):
LI Linze;  ZHANG Tao
Department of Automation, Tsinghua University, Beijing 100084, China
Keywords:
spatial noncooperative targets; feature detection and recognition; deep learning; R-FCN; light-head R-CNN; modified mask R-CNN; dataset construction; transfer learning
CLC:
TP18;V19
DOI:
10.11992/tis.202006011
Abstract:
To meet the intelligence requirements of feature detection and recognition of a spatial noncooperative target, the deep learning method—Mask Region-based Convolutional Network (Mask R-CNN)—is applied to the task using the idea of Region-based Fully Convolutional Network (R-FCN) and Light-head R-CNN as references to improve the detection speed to meet the real-time requirements of spatial tasks. Results obtained from the aforementioned study shows that the modified Mask R-CNN can shorten the detection time by 20% compared with the original version. Considering that deep neural networks require large sample datasets for training, this paper proposes a method for constructing a virtual environment for sample collection to build datasets, thus constructing the feature detection of spatial objects and datasets for recognition. The experimental result shows that the network learns the corresponding characteristics very well on the database generated from the virtual environment, and thus, the network can be migrated to the real task.
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