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R-CNN-Based Satellite Components Detection in Optical Images
Joint Authors
Chen, Yulang
Gao, Jingmin
Zhang, Kebei
Source
International Journal of Aerospace Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-05
Country of Publication
Egypt
No. of Pages
10
Abstract EN
The accurate detection of satellite components based on optical images can provide data support for aerospace missions such as pointing and tracking between satellites.
However, the traditional target detection method is inefficient when performing calculations and has a low detection precision, especially when the attitude of the satellite and illumination conditions change considerably.
To enable the precise detection of satellite components, we analyse the imaging characteristics of a satellite in space and propose a method to detect the satellite components.
This approach is based on a regional-based convolutional neural network (R-CNN), and it can enable the accurate detection of various satellite components by using optical images.
First, on the basis of the Mask R-CNN, we combine the DenseNet, ResNet, and FPN to construct a new feature extraction structure and obtain the R-CNN based satellite-component-detection model (RSD).
The feature maps are extracted and concatenated at a deeper multiscale level, and the feature propagation between each layer is enhanced by providing a dense connection.
Next, an information-rich satellite dataset is constructed, which is composed of images of various kinds of satellites from various perspectives and orbital positions.
The detection model is trained and optimized on the constructed dataset to obtain the satellite component detection model.
Finally, the proposed RSD model and original Mask R-CNN are tested on the same established test set.
The experimental results show that the proposed detection model has higher precision, recall rate, and F1 score.
Therefore, the proposed approach can effectively detect satellite components, based on optical images.
American Psychological Association (APA)
Chen, Yulang& Gao, Jingmin& Zhang, Kebei. 2020. R-CNN-Based Satellite Components Detection in Optical Images. International Journal of Aerospace Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1168272
Modern Language Association (MLA)
Chen, Yulang…[et al.]. R-CNN-Based Satellite Components Detection in Optical Images. International Journal of Aerospace Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1168272
American Medical Association (AMA)
Chen, Yulang& Gao, Jingmin& Zhang, Kebei. R-CNN-Based Satellite Components Detection in Optical Images. International Journal of Aerospace Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1168272
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1168272