## 1 Introduction
[32] based on the YOLO model, constructed a dataset using simulation images generated by Satellite Tool Kit (STK) software and physical model images produced by SolidWorks software, achieving accurate identification of satellites and their components. However, the study relied on simulated images to train the model and did not test it on real satellite images. While this approach provides a viable alternative when data are scarce, it does not validate the model's performance in handling complex real-world situations, potentially limiting the model's generalizability. [PERSON] et al. [33] proposed an instance segmentation model for satellite component segmentation and recognition based on Mask R-CNN, which improved the precision of satellite component recognition by enhancing the residual model with depthwise separable convolutions. [PERSON] et al. [34] built a new feature extraction network by combining Densely Connected Convolutional Networks (DenseNet), a Residual Network (ResNet), and a Feature Pyramid Network (FPN) on the basis of Mask R-CNN for the precise detection of satellite components, achieving high-accuracy identification and localization of satellite parts. Although these improvements based on Mask R-CNN enhanced the model's performance, they require high-performance hardware support for actual deployment, making them unsuitable for resource-constrained environments. [PERSON] et al. [35] established a space simulation environment based on Unreal Engine 4. After collecting 33 different satellite models, they generated a satellite simulation image dataset. They used a deep learning method based on ConvNeXt-Base for satellite component recognition and tested it on real satellite images. This approach is innovative and practical. However, the dataset lacks a sufficient variety of satellite types, which limits the model's ability to be well-trained for generalization. [PERSON] et al. [36] constructed a failed satellite dataset using a 1:1 model of the Chang'e satellite and proposed a method for detecting failed satellite components based on an improved Faster R-CNN. By integrating the modified High-Resolution Network (M-HRNet) into Faster R-CNN, they successfully improved the detection accuracy of failed satellite components. However, the dataset contains components from only one type of satellite, which limits the model's generalization capability. The detection algorithms for satellite components based on deep learning offer advantages over traditional algorithms in terms of faster speed, higher precision, and stronger robustness. However, due to the limited storage space and computational resources in the space environment, large-scale network models with a vast amount of parameter count and computational load are difficult to deploy in space, and it is challenging to meet the requirements for real-time performance. This poses challenges to the typical satellite component detection algorithms based on deep learning. This requires the algorithm to have a lower parameter count and lower computational load while maintaining high detection accuracy and speed. Additionally, in terms of dataset construction, issues such as the dataset scale being too small, a lack of real satellite images, and insufficient diversity in types still exist. To address the issues mentioned above, this paper proposes a lightweight typical satellite component detection algorithm based on the improved YOLOv8, aiming to enhance the accuracy and real-time performance of detection while reducing the model's parameter count and computational load. Furthermore, a larger-scale Typical Satellite Components Dataset (TSC-Dataset) has been constructed to provide effective sample support for model training. The contributions of this paper can be summarized as follows:  
1. A typical satellite component dataset was established, consisting of 3800 labeled satellite images. This is known to be the largest dataset of typical satellite components.