## 1 Introduction  
With the rapid development of space technology, various countries have launched a large number of satellites into space. According to the Satellite Database published by the Union of Concerned Scientists (UCS) [1], as of May 2023, there are 7560 artificial satellites in Earth's orbit. On-orbit services based on satellites have become an important development direction in space technology. Tasks such as satellite-based autonomous rendezvous and docking, space target capture, refueling, and on-orbit maintenance (as shown in Figure 1) are becoming increasingly important. These tasks require the accurate identification of typical satellite components to obtain the position and attitude information of the satellites [2; 3; 4]. Identifying typical satellite components is part of remote sensing technology, aiming to accurately recognize components such as solar panels, antennas, and the main body. This is crucial for identifying cooperative satellites (usually referring to satellites with known configurations that can accept control) and non-cooperative satellites (usually referring to satellites with unknown configurations that are unwilling or unable to accept control) [5; 6]. For example, in satellite formation flying [7], accurately identifying the features of each satellite can ensure precise positioning and coordinated operation of the formation. In space debris management [8], satellite feature recognition can help track and classify space debris, thereby formulating effective cleanup strategies. In emergency situations, feature recognition technology can quickly determine the location and status of malfunctioning satellites, providing critical data to support repair or replacement missions. Satellite feature recognition technology not only enhances the efficiency and safety of space missions but also lays the foundation for future space exploration and utilization. Remote sensing technology is currently widely applied in various fields such as Earth observation, environmental monitoring, and disaster warning. However, current remote sensing technology still faces many challenges in handling typical satellite component detection tasks. Firstly, the current datasets of typical satellite components are relatively small, lack diversity in satellite types, and lack real satellite images, which limits the effectiveness of model training. Secondly, traditional detection models are highly complex, making it difficult to apply them in resource-constrained environments. Therefore, developing large-scale datasets of typical satellite components and lightweight detection models has become a research focus in the field of remote sensing. At present, algorithms for detecting satellite components are mainly divided into traditional algorithms and deep learning algorithms. [PERSON] et al. [9] adopted a gradient-based line segment detection algorithm to rapidly detect the triangular supports of satellite solar panels. However, this method relies on the complete extraction and merging of line segments, which can be challenging in complex or noisy environments, potentially leading to false detections and decreased detection performance. [PERSON] et al. [10] proposed a method that uses Hough transform and edge detection to identify the backboard frame of satellite antennas. Figure 1: On-orbit service schematic: (**a**) rendezvous and docking; (**b**) on-orbit servicing; (**c**) target capture; (**d**) fuel resupply. Although Hough transform and edge detection are effective image processing tools, they can consume a significant amount of computational resources when identifying shapes in images, especially when dealing with high-resolution or complex images. This could lead to delays when processing real-time data. [PERSON] et al. [11] proposed a method for space non-cooperative target recognition based on maximum outer contour identification. This method is efficient and robust in image enhancement, contour extraction, and both near- and far-distance measurements.