# Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
## 2 Related Work  
### Image-Based Space On-Orbit Service Technology  
In recent years, on-orbit service concepts, such as space debris removal, satellite acquisition, and spacecraft construction, have attracted more and more attention from academia and industry [9]. Space technology, such as spacecraft object detection and on-orbit service based on image processing, has gradually contributed to the research field of space object monitoring. The image processing technology is rapidly driving the progress of space technology. [PERSON] et al. proposed an algorithm for adaptive real-time detection of faint optoelectronic geosynchronous orbit (GSO) debris [22] to detect a large amount of space debris in GSO that threatens the safety of spacecraft [23; 24]. Through image adaptive fast registration and dilated difference algorithms, combined with mathematical morphology, threshold segmentation, and global nearest neighbor (GNN) multi-target tracking algorithms, [PERSON] et al. achieved image background suppression, alignment, suspected target extraction, and multi-object tracking. This method can detect dim geostationary Earth orbit (GEO) and non-GEO debris in GSO. [PERSON] et al. designed an image-based visual servo system [25]. They used the visual servo system for the first time to successfully track a super-orbital re-entry of a spacecraft and record its spectrum feature. They optimized the visual servo system by adding a simplified feedforward control dynamic model, and they verified the tracking performance of the system on the International Space Station (ISS). [PERSON] et al. started research on monocular vision-guided on-orbit service technology in 2015 [26]. According to the explicit on-orbit service mission requirements of the United States and other countries [24; 27], to approach space objects in an energy-efficient, safe, and accurate way, they began to explore relying on three-dimensional computer models and a single two-dimensional image for the initial pose estimation of space objects. They wanted to estimate the initial pose without using reference marks, without using any distance measurements or any prior relative motion information. Since then, the research in space object monitoring has also started to evolve from the detection and tracking of space objects to the precise acquisition of object position and attitude. This is very consistent with the direction of rigid body 6D pose estimation, which deep learning has rapidly developed in recent years. Almost all the pose estimation methods need to separate the object from the complex background, so most methods are segmentation-driven pose estimation. The precise mask of the object is crucial for subsequent processing, such as keypoint detection and pose discriminator [28]. In 2019, Stanford University and the ESA hosted the Satellite Pose Estimation Challenge, limiting input data to two-dimensional images and moving away from object 3D model data. This competition is very suitable for the application scenario where the object is a non-cooperative spacecraft without a target and the competition is also very challenging. [PERSON] et al. proposed that the pose estimation of spacecraft could be divided into positioning and attitude determination, and the position error and attitude error were compared, respectively [9]. The classic monocular image-based space object positioning method iteratively solves the object position by extracting the shape context of the target, such as Harris corners, Canny edges, Hough transform, SIFT, SURF, and ORB features [29; 30; 31; 32; 33; 34]. The WeakGradient Elimination (WGE) technique was introduced by [PERSON] et al. [35]. It uses simple geometric constraints to synthesize the detected features to separate the edge features of the spacecraft from the weak edge features of the background. This method greatly reduces the search space of the feature correspondence problem. A more effective approach in recent years has been the use of deep neural network methods.