# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 6 Conclusions  
In this paper, an end-to-end spacecraft image segmentation network was proposed. Different types of spacecraft objects were segmented with monocular images as inputs to obtain their corresponding masks, and good segmentation results were achieved. We designed a neural network based on a dilated convolution and encoder+ attention+ decoder structure using the self-supervised learning method. Through the dilated convolutional and parallel-ASPP structure, our network enhanced the contextual information and mitigated the effects of gridding artifacts. In addition, the channel attention mechanism was introduced to recalibrate the features and improve the learning ability of the network. Our method is more robust to noise in the image and can segment complete and smooth object masks. We finely labelled public spacecraft datasets to establish a spacecraft segmentation dataset. We conducted various comparison experiments and an ablation study on the dataset. The experimental results show that our method outperforms other methods and has a better segmentation performance for spacecraft objects. In the future, we hope to improve the effect of spacecraft segmentation by the feature matching method in small-sample segmentation technology. At the same time, we would like to experiment with the deep unsupervised active learning strategy [58]. This allows the network to continuously acquire new knowledge for learning during the test phase. We also hope to build more complex space object segmentation datasets and evaluate our network more comprehensively.  
Our work will contribute to the research on on-orbit assembly in space. It is helpful for the space manipulator to move quickly to the vicinity of the spacecraft through the image, which provides a basis for further target detection and docking missions.  
Figure 14: Comparison diagram of similar structures of different spacecraft.  
**Author Contributions:** [PERSON] and [PERSON] designed this study; [PERSON] and [PERSON]. ([PERSON]) contributed to the theory research; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON]. ([PERSON]) analyzed the data; [PERSON] and [PERSON] wrote the paper and created the diagrams; [PERSON] and [PERSON] contributed to scientific advising and proofreading. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Science and Technology Department of Jilin Province, China: 20200401123 GX.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The SPEED dataset and the URSO dataset are made publicly available for research purposes. For more information, please refer to the websites [[https://kelvins.esa.int/satellite-pose-estimation-challenge/data/](https://kelvins.esa.int/satellite-pose-estimation-challenge/data/)]([https://kelvins.esa.int/satellite-pose-estimation-challenge/data/](https://kelvins.esa.int/satellite-pose-estimation-challenge/data/)) (accessed on 4 May 2021) and [[https://zenodo.org/record/3279632](https://zenodo.org/record/3279632)]([https://zenodo.org/record/3279632](https://zenodo.org/record/3279632)) (accessed on 21 June 2021). For the data license, please refer to the websites [[https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode](https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode)]([https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode](https://creativecommons.org/licenses/by-nc-sa/3.0/legalcode)) (accessed on 20 May 2022) and [[https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode)]([https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode)) (accessed on 20 May 2022).  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their constructive comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.