# Aircraft Target Interpretation Based on SAR Images
## 4 Experimental Results and Analysis
### Ablation Experiments  
To verify the effectiveness of MRFCA, we placed it after the feature layer of the backbone of YOLOv5s. Considering that the MRFCA module will affect the size of model parameters and infer speed to some extent, this paper only introduced one MRFCA module, which is placed after the \(C2\), \(C3\), \(C4\), or \(C5\) feature layer. Their respective recognition accuracy is shown in Table 4. Placing it after the \(C2\) feature layer can achieve the best recognition accuracy, the \(mAP_{50-95}\) has increased by 6.5%, and the \(mAP_{50-95}\) for S-Target has increased by 9.1%. This paper proposed to place the MRFCA module after the \(C2\) feature layer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & \(mAP_{50}\) & \(mAP_{75}\) & \(mAP_{50-95}\) & \
\begin{tabular}{c} **S-Target** \\ \(mAP_{50-95}\) \\ \end{tabular} &
\begin{tabular}{c} **L-Target** \\ \(mAP_{50-95}\) \\ \end{tabular} \\ \hline Faster R-CNN & ResNet-50 & 85.9 & 70.3 & 55.7 & 46.1 & 59.8 \\ \hline Retina-Net & ResNet-50 & 81.2 & 66.2 & 52.2 & 43.2 & 57.2 \\ \hline SSD & VGG-16 & 80.5 & 65.3 & 51.8 & 41.8 & 56.9 \\ \hline YOLOv3 & DarKnet-53 & 84.4 & 70.3 & 58.3 & 49.0 & 62.6 \\ \hline YOLOv5s & CSPDarknet53 & 85.1 & 71.6 & 61.0 & 50.5 & 65.9 \\ \hline Ours & Improved & 91.4 & 79.3 & 70.3 & 63.6 & 72.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative analysis based on different network model. To further prove that the proposed method improves the detection and recognition ability of YOLOv5s, we set up several groups of model comparison experiments. The first group is the prototype of YOLOv5s. The other experiments incorporated methods of MRFCA, 4 DDH, data augmentation, and K-means++, respectively, and all other structures are the same as YOLOv5s. The experimental results are shown in Table 5.  
On comparing Figures 9b and 9c, the YOLOv5s algorithm misses five aircraft targets when the target is small and the background is complex, but the improved YOLOv5s algorithm has a better extraction ability of image features, there are no cases of missed detection, it has better detection and recognition performance for aircraft targets in SAR images. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**C2** & **C3** & **C4** & **C5** & \(mAP_{\textbf{50}}\) & \(mAP_{\textbf{75}}\) & \(mAP_{\textbf{50-950}}\) & \
\begin{tabular}{c} **S-Target** \\ \(mAP_{\textbf{50-95}}\) \\ \end{tabular} &
\begin{tabular}{c} **L-Target** \\ \(mAP_{\textbf{50-95}}\) \\ \end{tabular} \\ \hline \(\times\) & \(\times\) & \(\times\) & \(\times\) & 85.1 & 71.6 & 61.0 & 50.5 & 65.9 \\ \hline \(\surd\) & \(\times\) & \(\times\) & \(\times\) & 89.3 & 76.2 & 67.5 & 59.6 & 69.9 \\ \hline \(\times\) & \(\surd\) & \(\times\) & \(\times\) & 89.0 & 75.8 & 66.9 & 59.0 & 69.6 \\ \hline \(\times\) & \(\times\) & \(\surd\) & \(\times\) & 88.6 & 75.3 & 66.2 & 58.2 & 69.2 \\ \hline \(\times\) & \(\times\) & \(\times\) & \(\surd\) & 88.1 & 74.8 & 65.3 & 57.0 & 68.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of recognition accuracy of MRFCA modules.