# Intelligent Space Object Detection Driven by Data from Space Objects
## 2 Dataset Construction and Labeling
### Dataset Labeling  
Dataset labeling plays a crucial role in the construction of a dataset, which has a significant impact on the final outcome of training. Typically, dataset labeling is carried out manually by using various open-source labeling tools like labelimg. As for image object detection, it requires the areas of interest in the images to be manually marked, including the coordinates, sizes, and categories of the objects. However, this process is labor-intensive, time-consuming, and often inefficient. In this study, there is a specific challenge encountered in satellite object detection, requiring the use of a large-scale dataset with different classes and object poses. Unfortunately, the available satellite images are not labeled, which necessitates manual labeling efforts. To reduce the workload, semi-automatic labeling is performed. To begin with, a lightweight model is trained on a subset of already labeled data, which facilitates the automatic labeling of the remaining unlabeled data [33]. Subsequently, the automatically generated labels are manually corrected to rectify any mislabeling or omissions. Through this approach, both labor and time costs are significantly reduced. Meanwhile, the large-scale acquisition of high-quality labeled data is promoted, which is essential for the development of an effective satellite object detection model. In this paper, a total of 1000 images are manually labeled. There are five categories of label for each image: \"Panel\", \"Body\", \"Antenna\", \"Optical-load\", and \"Antenna-rod\", as shown in Table 1. To improve the effectiveness of semi-automatic labeling, dozens of images are randomly labeled for each category of satellites, which improves dataset distribution for the image categories to be balanced. Considering the similarities in appearance between airplanes and satellites, the parameters learned by the model on a remote sensing airplane dataset are effectively transferred for the training on a satellite dataset. Moreover, due to the scarcity of manually annotated datasets, transfer learning is conducive to accelerating the convergence of training satellite models. Thus, the performance in training is improved for smaller models. Named \"plane\", the model trained on the remote sensing airplane dataset using YOLOv5s reaches 0.99 in precision. This method of semi-automatic labeling relies on a pre-trained remote sensing aircraft model (plane) constructed through transfer learning [34]. It undergoes 300 iterations with a batch size of 86, resulting in a small model with a training accuracy of 0.852. Then, this model is used to predict the labels for 6300 unlabeled data points. Since the model is unfit for completely correct prediction, manual screening is performed to remove \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Solar Panels on** & **Body of the Satellite** & **Circular Antenna on** & **Optical Payload on** & **Antenna Array on** \\
**Satellite** & **Satellite** & **Satellite** & **Satellite** & **Satellite** \\ \hline panel & body & antenna & optical-load & antenna-rod \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite components and abbreviations reference table. Figure 6: Sixteen background images of a single satellite. the incorrectly labeled data. Additionally, human-computer interaction is performed to obtain accurately labeled high-quality datasets. In combination with the labeled datasets, they comprise a larger dataset for subsequent training. Semi-automatic labeling is suitable to transform the role of annotators from data labeling to data verification, thus enhancing work efficiency significantly. According to the experimental results, the lightweight model constructed by using plane as the model pre-trained on fewer samples performs better in detection [35]. The ultimate accuracy of labeling is consistently above 94%, with the best performance achieved on the label of \"panel\" given an accuracy of 0.98.