## 4 Numerical and Anechoic Chamber Experiments
### Comparisons with other algorithms  
To evaluate the detection performance fairly, the following representative algorithms are chosen for comparison: YOLOv3, YOLOv5, YOLOv6, YOLOv8 and YOLOv11. Figure 12 presents the outcomes of detecting objects using seven various detection networks. The images across each row illustrate the detection results from these networks at a set focal width. The boxes marked in red, pink, orange, and yellow indicate the identified areas of the satellite, the ellipse arc, the edge pair-points, and specular point, respectively. Observations reveal that ours successfully detects the parabolic antenna and satellite for all five samples, while others have varying degrees of shortcomings. YOLOv3, YOLOv5, YOLOv6, YOLOv8, and YOLOv11 miss the detection of the component in row 5, which indicates our network's advantage in recognizing small-scale targets. Meanwhile, YOLOv3 misses detection in row 3, and the identification outcomes of YOLOv5 yield numerous  
intersecting bounding boxes for the elliptical arc, suggesting that the network necessitates a more refined Non-Maximum Suppression (NMS) threshold. Tables 2-5 present performance metrics across various networks in the context of the satellite, the ellipse arc, the edge pair-points, and specular point quantitatively. From Table 2, we can tell that all networks detect the satellite accurately, which demonstrates stability in large target detection. Observing Table 3, it can be found that ours achieves the highest F1, R, mAP50, and mAP50-95, and the highest score of P is achieved by YOLOv5. In Table 4, we can see that the proposed algorithm achieves the top values across P, F1, mAP50, and mAP50-95 metrics, with YOLOv8 and YOLOv5 holding the top position for the R metric. In Table 5, the proposed algorithm also exhibits the highest mAP50 value, while YOLOv3 and YOLOv6, respectively, claim the top spots for the P and R metrics and YOLOv5 holds the top spots for the F1 and mAP50-95 metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Networks** & **P** & **R** & **F1** & **mAP50** & **mAP50-95** \\ \hline YOLOv3 & 0.968 & 1 & 0.984 & 0.994 & 0.679 \\ YOLOv5 & 0.996 & 1 & **0.998** & **0.995** & 0.871 \\ YOLOv6 & **1** & 0.98 & 0.99 & 0.995 & 0.766 \\ YOLOv8 & 0.986 & 1 & 0.993 & **0.995** & 0.824 \\ YOLOv11 & 0.996 & 0.989 & 0.981 & 0.994 & 0.794 \\ Ours & 0.996 & **1** & **0.998** & **0.995** & **0.873** \\ \hline \end{tabular}
\end{table} 
Table 2: The performance metrics across various networks in the context of satellite analysis. Figure 12: A comparison of the detection performance of different algorithms on EM data. In order to focus attention on the detection of parabolic dish antennas, by integrating the mAP50 and mAP50-95 values from the aforementioned tables, namely, Tables 3-5, we obtain the following histogram in Figure 13. As a result, the efficacy of the suggested approach achieves peak levels in the recognition outcomes for parabolic antenna. In order to gain a more detailed assessment of the detection capabilities, the precision-recall (PR) curves of the ellipse arc, the edge pair-points, and specular point are presented in Figure 14. For the ellipse arc, the proposed algorithm's advantage is not significant. But for the specular point and edge pair-points, our algorithm is situated in the upper right-hand side of all the curves, thus verifying the stability of the detection. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Networks** & **P** & **R** & **F1** & **mAP50** & **mAP50-95** \\ \hline YOLOv3 & 0.67 & 0.905 & 0.770 & 0.913 & 0.566 \\ YOLOv5 & 0.736 & **0.952** & 0.830 & 0.913 & 0.627 \\ YOLOv6 & 0.653 & 0.895 & 0.755 & 0.891 & 0.581 \\ YOLOv8 & 0.694 & **0.952** & 0.803 & 0.933 & 0.62 \\ YOLOv11 & 0.624 & 0.857 & 0.722 & 0.852 & 0.536 \\ Ours & **0.902** & 0.948 & **0.924** & **0.989** & **0.685** \\ \hline \end{tabular}
\end{table} 
Table 4: The performance metrics across various networks in the context of edge pair-points analysis.