## 1 Introduction
through multi-process and multi-task means. It is widely used to promote estimating precision and clarify processes. Its general processing flow [26] is as follows:  
* Satellite localization network (SLN): Using object detection networks to train satellite object detectors in order to achieve precise satellite localization results. * Landmark regression network (LRN): Input the object detected by SLN into the landmark regression network for training. * Pose solver: The detected satellite landmarks are solved for satellite poses using PnP solver. The UniAdelaide team [26] used the MMDetection network for object detection, then clipped out the object area and provided it to the HRNet [27] for landmark regression, and finally used PnP solver to calculate the pose information about satellite. Reference [28] used similar methods to detect satellite landmarks, whose processing was similar to the above methods, and different deep learning models were used in satellite localization network and landmark regression network. In reference [28], the transformer model was used, but it was only used in traditional landmark regression tasks. This method indicates that the model estimating process was divided into four steps, and the total processing stage took 212.1 ms. The pose estimation of non-cooperative space objects based on key point detection can detect their pose information with high accuracy, but there are problems such as being very time-consuming, having a complicated detection process [26; 28; 29], etc. It requires multiple steps and models to detect relevant information and requires obvious key points of non-cooperative space objects; otherwise, it is difficult to measure the CAD model and key points. As end-to-end methods based on deep learning are widely used in industry, especially in pose estimation tasks of six degrees of freedom, deep learning models [30; 31; 32] based on convolutional neural networks are applied to pose estimation problems. However, convolutional neural networks have a strong feature extraction ability [33; 34], but their remote modeling ability is poor, and only focusing on the feature pixels around the current pixel has limitations. Since the use of the transformer model in speech recognition in 2017, it has received extensive attention from industry and academia, and the model has achieved great success in timing information features such as natural language processing and speech recognition. Its core component, a self-attention mechanism, has powerful feature extraction and temporal correlation ability; that is, it highlights the advantages of convolutional neural network and recurrent neural network. Subsequently, transformer models led by vision transformer [35; 36] and detection transformer (DETR) [37] have achieved excellent results in the field of computer vision, especially in image recognition and object detection. In reference [28], the transformer model is applied to satellite pose estimation, but this work only uses the transformer model for satellite landmark regression, whose function is consistent with the key point detection of the DETR model, and it fails to directly output the satellite pose information through an end-to-end learning structure. Combined with the advantages of the transformer model, this paper needs to explore an end-to-end pose estimation method based on the transformer model. Aiming to solve the problems of the complex detection process, prior knowledge, and long detection time in existing non-cooperative space object pose estimation, this paper explores the application of the transformer model in the pose estimation of non-cooperative space objects and designs an efficient pose estimation algorithm of non-cooperative space objects based on dual-channel transformer.