# Intelligent Space Object Detection Driven by Data from Space Objects
## 1 Introduction  
With the constant progress in space exploration, there has been a significant advancement of such technologies as positioning and navigation, satellite communication, remote observation, surveillance, and reconnaissance, all of which rely on the use of satellites. At present, they play an increasingly important role in both civil engineering and national defense. However, the accumulation of space debris and retired satellites poses a severe threat to operational satellites. Therefore, it is imperative to minimize the safety risk of in-orbit satellites by enabling satellites to automatically identify the targets in the surrounding environment [1]. In recent years, various image sensors have been widely applied on satellites to enhance their capability to perform detection in the surrounding environment, which is significant to the development of image-based space object detection methods [2, 3]. In 2014, [PERSON] proposed that shape estimation can be treated as the most likely shape model, with each filter operating based on unscented estimation. Thus, the time brightness of resident space objects is combined with angular data to leverage fusion sensitivity, thus creating the models and associated trajectories of resident space objects. In 2016, [PERSON] used matched filters and Bayesian methods to detect the target in space. Subsequently, [PERSON] tracked and characterized both active and inactive space objects for the description and classification of features of space debris. In 2017, [PERSON] proposed to detect the objects through the correlation between the motion of objects over multiple frames and the attitude-related information about satellites. By using the video footage captured by the Tian Tuo-2 satellite, the algorithm showed a massive potential in space target detection. In 2018, [PERSON] pioneered applyingCNNs to spacecraft detection. Then, an improved regression-based convolutional neural network named YOLOv2 was adopted to detect the spacecraft in the images. In 2019, [PERSON] introduced a new method based on multi-model adaptive estimation to determine the most likely shape of resident space objects through multiple candidate shape models. Meanwhile, the observed inertial orientation and trajectory of resident space objects were recovered. This method proved effective in identifying the model and state of residential objects in space. Proposed by [PERSON], a space target recognition network (T-SCNN) was shown to achieve a high accuracy in recognition. In 2020, [PERSON] put forward a mixed R-CNN with partial semantic information for space target recognition, with the main components of detected satellites segmented. In 2021, the University of Luxembourg introduced the SPARK space satellite dataset as a novel and unique multimodal image dataset of space objects to classify spacecraft and debris into 11 categories in total. In 2022, [PERSON] presented the Spacecraft Pose Network (SPN), in which the regions within 2D bounding boxes were used to determine the relative pose. Then, regression was performed to obtain finer estimates. Through the SPN method, the relative positions were estimated by leveraging the constraints imposed by the detected 2D bounding boxes and the estimated relative poses [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. With the accumulation of data and the continuous development of artificial intelligence technology, deep learning has been widely practiced to improve the performance in object detection. In terms of generalized object detection, [PERSON] et al. pioneered proposing the deep convolutional neural network [14]. Subsequently, other researchers like [PERSON] integrated deep neural networks with image object detection to yield a series of effective works, progressing from R-CNN to Fast R-CNN, and further to Faster R-CNN [15; 16; 17]. These works laid a foundation for the development of classical architectures intended for object detection. However, the limitations of the R-CNN architecture make detection a time-consuming process in certain scenarios.