# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 1 Introduction  
Since the beginning of the 21 st century, human beings have accelerated the pace of space exploration, and space technology plays a vital role in it. Rapidly evolving computer vision and machine learning techniques have facilitated space technology development through applications in tasks such as collision avoidance self-navigation systems, spacecraft health monitoring, and asteroid classification [1, 2]. The on-orbit formation and operation of large space equipment, such as space stations and the work and maintenance of spacecraft, cannot be completed without the support of space technology. Performing space missions often requires spacecraft images acquired by vision sensors as inputs, using computer vision technology to determine spacecraft position and attitude information, and then performing complex tasks, such as spacecraft rendezvous and docking, space equipment assembly on-orbit, spacecraft grasping and maintenance, and space debris removal [3, 4, 5]. In recent years, many spacecraft have been launched into space, the number of space exploration missions has increased, and the low Earth orbit has become crowded. Defunct satellites and space debris are growing and urgently need to be cleaned up [6]. Spacecraft positioning and other related issues have attracted more and more attention from researchers in space technology. The successful segmentation of spacecraft objects in the image is the key tothe accurate positioning of spacecraft. The fine and precise spacecraft mask obtained after segmentation is conducive to the keypoint detection of the object, which is crucial for the realization of vision-based attitude estimation [7]. This paper investigates this issue. Spacecraft image segmentation is to separate the spacecraft object and background from the acquired image. Compared with object detection, spacecraft image segmentation can obtain more accurate position information of the object and obtain more precise object contour, which is more difficult to achieve. Considering the particularity of the environment of the on-orbit mission, the spacecraft image segmentation task needs to face more severe lighting conditions, such as low light, overexposure, and spacecraft reflection, than the general image segmentation task. The impact of complex background and noise on the mission also needs to be considered. All these problems put forward higher requirements for the robustness and accuracy of the segmentation algorithm. Spacecraft objects can be divided into cooperative and non-cooperative spacecraft in different types of missions [8]. Cooperative spacecraft may be positioned and docked using dedicated radio links, reference markers, backward reflectors, etc. Non-cooperative spacecraft may be unknown spacecraft that do not have cooperative conditions. Cooperative spacecraft are designed with cooperative targets for precise docking at the grasping position for the on-orbit assembly mission. However, before docking is performed, it is necessary to operate the manipulator appropriately to ensure that the cooperative marks of the spacecraft are within the field of view of the hand-eye camera. Using spacecraft image segmentation techniques, even if the target spacecraft is non-cooperative, the spacecraft position can be obtained as long as the appearance of the spacecraft is known. The European Space Agency (ESA) and Stanford University held a competition in 2019 to use supervised learning to estimate the position and attitude of a known spacecraft from an image [9]. This paper belongs to this research direction but only studies the positioning of known spacecraft through monocular images. In the past decade, deep learning has developed rapidly. Image segmentation tasks have achieved far better results than traditional machine learning methods through deep neural networks. Several branches, such as semantic segmentation, instance segmentation, and panoptic segmentation, have been developed. Image segmentation for known spacecraft is well suited for processing using deep learning methods.