## 5 Results
### Visualization Experiments  
In the visualization experiments, the performance and accuracy of the two models, YOLOv8s and LTSCD-YOLO, can be intuitively compared through their P-R curves. The detection results of YOLOv8s are shown in Figure 13(a), while those of LTSCD-YOLO are displayed in Figure 13(b). From these figures, it's observable that LTSCD-YOLO achieves an mAP50 of 97.6% for solar panel detection, which is an 0.83% increase compared to YOLOv8s; an mAP50 of 87.4% for antenna detection, a 2.82% increase; and an mAP50 of 98.5% for main body detection, a 1.13% increase. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **P/\%** & **R/\%** & **[EMAIL_ADDRESS]**\%** & **Params/\(\mathbf{10^{6}}\)** & **GFLOPs/G** & **FPS/(frame\(\cdot\)s\({}^{-1}\))** \\ \hline YOLOv3-tiny & 88.7 & 83.3 & 88.7 & 12.13 & 18.9 & **143.85** \\ YOLOv4-CSP & 89.4 & 84.2 & 89.7 & 52.50 & 119.7 & 72.15 \\ YOLOv5s & 93.8 & 86.8 & 92.4 & 7.03 & 16.0 & 86.2 \\ YOLOv6n & 89.2 & 81.1 & 88.9 & 4.5 & 11.8 & 90.09 \\ YOLOv7-tiny & 94.2 & 85.8 & 92.0 & 6.02 & 13.2 & 85.29 \\ YOLOv7 & 94.6 & 89.4 & 93.8 & 36.49 & 103.2 & 74.86 \\ YOLOv8s & 92.9 & 88.2 & 93.1 & 11.14 & 28.8 & 98.04 \\ YOLOv8s-ghost & 93.6 & 87.0 & 91.9 & 5.92 & 16.1 & 90.91 \\ YOLOv9t & 92.1 & 88.4 & 92.9 & 2.62 & 10.7 & 50.36 \\ YOLOv9s & 95.0 & **89.9** & 94.3 & 9.60 & 38.7 & 41.67 \\ YOLOv10s & 93.4 & 87.9 & 93.3 & 8.04 & 24.5 & 97.59 \\ \hline RT-DETR-ResNet50 & 92.4 & 87.2 & 92.3 & 42.0 & 130.5 & 22.69 \\ RT-DETR-ResNet101 & 93.0 & 88.3 & 93.2 & 61.76 & 191.4 & 16.89 \\ DETR-L & 92.3 & 87.2 & 92.2 & 32.8 & 108.0 & 25.35 \\ DETR-X & 92.4 & 89.2 & 93.5 & 67.3 & 232.3 & 15.68 \\ \hline
**Ours** & **95.7** & 88.2 & **94.5** & **2.40** & **9.8** & 115.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results of different models. A confusion matrix is a tool used to evaluate model performance. It reflects the relationship between the model's prediction and the actual situation of sample data, thereby assessing the model's classification performance. In a confusion matrix, the horizontal axis represents the actual labels, and the vertical axis represents the predicted labels. Elements on the diagonal indicate perfect agreement between predictions and actual outcomes, while other elements show the number of samples misclassified. Ideally, the confusion matrix would be a diagonal matrix, indicating outstanding classification ability. Figure 15a shows the confusion matrix obtained from training YOLOv8s, and Figure 15b shows the one from LTSCD-YOLO. The comparison of confusion matrices shows that the LTSCD-YOLO model performs better in reducing missed and false detections. The relationship between actual and predicted values in the LTSCD-YOLO model's confusion matrix closely follows the characteristics of a diagonal matrix, thereby proving the model's superior performance. To investigate the model's detection performance on different typical satellite components, we selected 360 satellite images for testing. From Table 6, it can be seen that the precision rate of LTSCD-YOLO in solar panel detection is 96.0%, an increase of 0.21% compared to YOLOv8s; the recall rate is 93.1%, an increase of 1.31% compared to YOLOv8s. In antenna detection, the precision rate is 88.8%, an increase of 2.19% compared to YOLOv8s; the recall rate is 83.2%, an increase of 5.45% compared to YOLOv8s. In main body detection, the precision rate is 96.4%, an increase of 0.42% compared to YOLOv8s; the recall rate is 96.8%, an increase of 3.31% compared to YOLOv8s. It can be seen that LTSCD-YOLO,  
Figure 14: Comparative images of the detection results: (**a**) YOLOv8s detection results and (**b**) LTSCD-YOLO detection results. Figure 15: Comparative images of the confusion matrices: (**a**) YOLOv8s confusion matrix and (**b**) LTSCD-YOLO confusion matrix.