## 3 Construction of the Experimental Dataset
The outlines and colors of these local components change with the satellite's attitude and orbital position. The imaging characteristics of space objects include variability of viewing angles, blurriness of images, and uneven brightness, among others. To simulate real imaging conditions in the space environment, improve the model's generalization and robustness, and enhance the model's diversity and adaptability, this paper employs data augmentation techniques to expand the training set. The specific augmentation operations include:  
Rotation: Each image has a 50% probability of being rotated, with the rotation angle randomly selected between \(-10^{\circ}\) and \(10^{\circ}\). This simulates variations in satellite images caused by observations from different angles. Translation: Each image has a 30% probability of being translated horizontally or vertically. The translation distance is determined as a certain proportion of the image size, simulating displacement errors during the imaging process. Figure 11: Typical satellite components diagram: (**a**) solar panel and (**b**) antenna. Figure 10: Schematic diagram of satellite solar panel, main body, and antenna. Mirroring: Each image has a 40% probability of being mirrored horizontally or vertically, further increasing the diversity of perspectives. Color Adjustment and Scaling: Color adjustments include modifying brightness and saturation, while scaling involves changing the image size. Both transformations have a probability of 45%, adapting to different lighting conditions and resolution changes. Noise Addition and Brightness Adjustment: Given the common occurrence of uneven brightness and noise in space environments, these two augmentation operations are set with a probability of 70%. The noise added is Gaussian noise; for brightness adjustment, a random value _alpha_ between 0.35 and 1.25 is set to determine the brightness level during the data augmentation process. A lower _alpha_ value darkens the image, while a higher value brightens it. Each data augmentation transformation depends on an independent random probability. Therefore, an image can undergo multiple operations such as rotation, translation, and noise addition in one data augmentation process, significantly increasing the diversity of the data. This helps in training a more robust model. The number of data augmentations for each image in the training set is set to 5. After data augmentation, the training set expands from 2520 images to 15,120 images. The number of images in the validation and test sets remains unchanged, resulting in a total of 16,400 satellite images. The augmented images are shown in Figure 12.  
To intuitively and conveniently display the distribution characteristics of different types and sizes of typical satellite components in the dataset, we conducted a detailed statistical analysis of the annotation files of the TSC-Dataset. The analysis results are shown in Figure 13, which includes four parts: Figure 13a presents a histogram of the distribution of the number of categories in the training set, from which one can intuitively understand the quantity situation of each type of component; Figure 13b visually shows the distribution of the length and width of all annotation boxes by setting the center points \(x\), \(y\) coordinates of all annotation boxes at the same position, helping to observe the characteristics of size distribution; Figure 13c is a histogram of the \(x\), \(y\) coordinates used to analyze the distribution of annotation box center points in the image; and Figure 13d shows the histogram of the width and height variables of the annotation boxes. Figure 12: Example of data augmentation: (**a**) original image and (**b**) images after data augmentation.