Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation

In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and spac...

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Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 11; p. 4222
Main Authors:	Liu, Yuan, Zhu, Ming, Wang, Jing, Guo, Xiangji, Yang, Yifan, Wang, Jiarong
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01.06.2022 MDPI
Subjects:	Algorithms Annotations Datasets Deep learning DeepLabv3 dilated convolution Discovery and exploration Image processing Machine learning Methods multi-scale Neural networks Outer space Remote sensing Satellites semantic segmentation Semantics Space exploration Space ships Space vehicles China deep learning DeepLabv3 semantic segmentation dilated convolution multi-scale
ISSN:	1424-8220, 1424-8220
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Abstract	In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.
AbstractList	In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework. In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.
Audience	Academic
Author	Liu, Yuan Guo, Xiangji Yang, Yifan Wang, Jing Wang, Jiarong Zhu, Ming
AuthorAffiliation	2 School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China 1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; liuyuan18@mails.ucas.ac.cn (Y.L.); wangjing@ciomp.ac.cn (J.W.); guoxiangji18@mails.ucas.ac.cn (X.G.); yangyifan17@mails.ucas.ac.cn (Y.Y.); wangjiarong@cust.edu.cn (J.W.)
AuthorAffiliation_xml	– name: 1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China; liuyuan18@mails.ucas.ac.cn (Y.L.); wangjing@ciomp.ac.cn (J.W.); guoxiangji18@mails.ucas.ac.cn (X.G.); yangyifan17@mails.ucas.ac.cn (Y.Y.); wangjiarong@cust.edu.cn (J.W.) – name: 2 School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
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StartPage	4222
SubjectTerms	Algorithms Annotations Datasets Deep learning DeepLabv3 dilated convolution Discovery and exploration Image processing Machine learning Methods multi-scale Neural networks Outer space Remote sensing Satellites semantic segmentation Semantics Space exploration Space ships Space vehicles
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Title	Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
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