## 1 Introduction
However, it requires significant computational resources and relies on outer contour features, which can affect its measurement accuracy in complex environments with varying lighting conditions and noise. Traditional satellite component detection methods generally extract features for a certain type of satellite component, requiring the setting of a large number of hyperparameters in advance. Therefore, traditional feature extraction algorithms have difficulties in optimization, poor generalizability, and unsatisfactory detection accuracy and efficiency. In recent years, with the rapid development of deep learning technology, object detection algorithms based on deep learning have made significant progress. Based on whether there is a preprocessing step of candidate region selection, these algorithms can be divided into two-stage and one-stage object detection algorithms. Two-stage object detection primarily divides the detection process into two steps: (1) finding candidate regions in a given image that contain target objects and (2) performing classification and regression on these candidate regions to obtain the detection results. In terms of region extraction, early two-stage detection algorithms mainly used selective search techniques, such as R-CNN [12], SPPNet [13], and Fast R-CNN [14], which were slow and lacked practicality; Faster R-CNN [15] introduced a method based on Region Proposal Network (RPN), greatly enhancing the speed and accuracy of region extraction. From the aspect of feature utilization, the introduction of the Feature Pyramid Network (FPN) structure merges high-level semantic information with low-level spatial information, improving the detection performance for small objects, as seen in algorithms like Mask R-CNN [16]. One-stage object detection algorithms directly extract features through a Convolutional Neural Network (CNN) and predict both the classification and localization of objects simultaneously. The YOLO algorithm proposed in the literature [17] was among the first one-stage object detection algorithms with practical application value. The success of the YOLO algorithm has attracted the attention of numerous researchers, leading to successive improvements in the form of YOLOv2 [18], YOLOv3 [19], YOLOv4 [20], YOLOv5 [21], YOLOv6 [22], YOLOv7 [23], YOLOv8 [24], YOLOv9 [25], and YOLOv10 [26]. Deep learning-based object detection algorithms are widely applied in the field of remote sensing. [PERSON] et al. [27] proposed a lightweight YOLO network based on GhostConv, which achieves precise detection of small targets in unmanned aerial vehicle images. [PERSON] et al. [28] proposed a lightweight forest fire smoke detection model based on YOLOv7, which achieves high detection accuracy with fewer computational resources. [PERSON] et al. [29] proposed a boundary box synthetic aperture radar ship detection model based on YOLOv7, effectively addressing challenges in synthetic aperture radar ship detection, achieving high-precision results, and improving accuracy and computational efficiency. Object detection algorithms based on deep learning are also widely used in the detection of typical components in satellites. [PERSON] et al. [30] proposed a space object recognition method based on the Deep Convolutional Neural Network (DCNN), which solved the problem of insufficient datasets through data augmentation, thereby achieving satellite recognition. Although this method achieved high accuracy on synthetic space target datasets, its performance on real space image data still needs to be verified. Synthetic datasets may not fully capture the complexity of real-world application environments, so the model's performance in real scenarios may face challenges. [PERSON] et al. [31] proposed an object feature point extraction method based on Faster R-CNN to detect and locate satellite docking nodes. Due to high model complexity, the training and inference processes require significant computational resources, making it unsuitable for resource-constrained devices. [PERSON] et al.