## 4 Experimental Detail
### Experimental Evaluation Criteria  
To accurately analyze the model's detection performance on typical satellite components in a space environment, we selected Precision (\(P\)), Recall (\(R\)), Mean Average Precision (\(mAP\)), model parameter count (\(Params\)), model computation load (\(GFLOPs\)), and frames per second (\(FPS\)) as evaluation metrics to assess the overall performance of the model.  
Precision (\(P\)) is an indicator measured based on the prediction results, representing the proportion of actual positive cases among all samples predicted as positive. In the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Details** \\ \hline Operating system & Ubuntu18.04 \\ GPU & NVIDIA GeForce RTX 3090, 24 GB \\ CPU & Intel Xeon Gold 6248R \\ Memory & 125 G \\ Programming language & Python38.18 \\ Framework & PyTorch 1.11.0 + CUDA A1.3 + cuDNN 8.2.0 \\ IDE & PyCharm2022.3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of the experimental environment.  
Figure 13: Visualization of dataset annotation files: (**a**) dataset category histogram; (**b**) the length and width distribution map of annotation boxes in the dataset; (**c**) histograms of variables \(x\) and \(y\); (**d**) variable width and height histograms.  
prediction results, samples deemed positive can be divided into two categories: True Positive (\(TP\)) and False Positive (\(FP\)). A prediction is considered positive if the sample's \(IoU\) is greater than or equal to a specific confidence threshold; otherwise, if the \(IoU\) is less than this threshold, the prediction is deemed negative. Precision can be represented by Equation (10).  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
Recall (\(R\)) is an assessment metric of the model's comprehensiveness in detection, indicating the proportion of samples correctly predicted as positive among all actual positive samples. The definition of correctly predicted as positive is \(TP\), and the definition of wrongly predicted as negative is \(FN\). Recall is represented by Equation (11).  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]  
By taking precision as the vertical axis and recall as the horizontal axis, the model's performance \(P-R\) curve can be obtained. The area under the \(P-R\) curve is the Average Precision (\(AP\)) for single-class labels, represented by Equation (12).  
\[AP=\int_{0}^{1}P(R)dR \tag{12}\]  
For detection targets of \(N\) categories, the mean of average precision across categories is represented by Equation (13). All \(AP\) that are averaged in the given equation were computed for the IoU threshold of 0.5.  
\[mAP=\frac{\sum_{n=1}^{N}AP_{n}}{N} \tag{13}\]  
\(mAP\) is one of the most important model performance evaluation metrics in the field of object detection, used to measure the model's detection accuracy and comprehensiveness across multiple categories.  
In real-world object detection scenarios, in addition to pursuing high accuracy, the model's detection speed is also very important. The model's detection speed is commonly measured by \(FPS\) (frames per second), which is the number of images the model can process per second.  
The model parameter count (\(Params\)) refers to the total number of all parameters during the model training process. This parameter count reflects the model's spatial complexity and overall scale, thereby becoming a key indicator to measure the model's lightweight level.  
The model computation load (\(GFLOPs\)) refers to the number of floating-point operations required for the model to perform one forward propagation process. It is an indicator to evaluate the model's consumption of computational resources.