# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 4 Experiments and Analysis
### Ablation Experiments  
To further prove the progressiveness and superiority of our proposed method, we performed several groups of ablation experiments. All the improved models in the experiment used YOLOv5s as the baseline. \"\(\times\)\" indicates that the corresponding improvement method was not used, and \"\(\surd\)\" indicates that the corresponding improvement method was used. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Boeing 787** & **Boeing 737** & **A220** & **ARj21** & **A330** & **A320/321** & **Others** & **All** \\  & \(AP_{90}\) & \(AP_{90}\) & \(AP_{90}\) & \(AP_{90}\) & \(AP_{90}\) & \(AP_{90}\) & \(AP_{90}\) & \(mAP_{90}\) \\ \hline Faster R-CNN & 0.911 & 0.852 & 0.849 & 0.876 & 0.801 & 0.858 & 0.867 & 0.859 \\ \hline Retina-Net & 0.850 & 0.815 & 0.809 & 0.843 & 0.758 & 0.796 & 0.811 & 0.812 \\ \hline SSD & 0.832 & 0.805 & 0.789 & 0.867 & 0.764 & 0.783 & 0.797 & 0.805 \\ \hline YOLOv3 & 0.898 & 0.911 & 0.873 & 0.916 & 0.744 & 0.852 & 0.871 & 0.866 \\ \hline YOLOv5s & 0.890 & 0.906 & 0.874 & 0.914 & 0.816 & 0.854 & 0.855 & 0.873 \\ \hline Improved YOLOv5s & 0.963 & 0.955 & 0.964 & 0.948 & 0.843 & 0.915 & 0.928 & 0.931 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of performance in terms of recognition. Figure 8: PR curves of the improved YOLOv5s algorithm. In this study, \(mAP_{50}\), \(mAP_{75}\), and \(mAP_{50-95}\) were used to evaluate the recognition precision. In addition, we considered other ways to evaluate the performance: parameter size of the model (\(PARAM\)), giga floating-point operations per second (\(FLOPs\)), and detection frames per second (\(FPS\)). The results are shown in Table 3.  
Experiment 1 used the original YOLOv5s model. The \(mAP_{50}\) was 0.873. To increase the precision of detection, experiment 2 incorporated the newly designed MAFA module, which improved the \(mAP_{50}\) to 0.912. However, the \(PARAM\) of the model was 11.5 M, which was higher than the original value (7.0 M), and the \(FPS\) was 111, which was slower than the original (116). Then, we considered how to improve the speed of detection and reduce the model's size. Firstly, we proposed using the loss function SIOU to replace the CIOU to improve the speed of detection. The effect was quite obviously proved by comparative experiments 1 and 3, and experiments 2 and 4. Experiment 3 only optimized the loss function SIOU, and the \(FPS\) was 127. Experiment 4 combined the MAFA module and SIOU, and the \(FPS\) was 118. The precision of detection was also slightly improved. Secondly, we designed the Ghost-Conv and SE-Ghost-C3 lightweight network, which reduced the number of parameters of the model by about 3 M in comparative experiments 1 and 5, and experiments 4 and 6. Experiment 5 only used the designed lightweight network, and the \(PARAM\) of the model was 4.1 M. Experiment 6 combined the MAFA module, SIOU, and the lightweight network, and the \(PARAM\) of the model was 8.4 M. The precision and speed of detection were almost unaffected. Finally, to further improve the accuracy of detection, the CA mechanism was incorporated into the model. Experiments 7 and 8 verified that the accuracy of detection improved to a certain extent compared with the original model. We used the final improved YOLOv5s structure in experiment 8. The accuracy of detection reached 0.931, which was 0.058 higher than that of the original YOLOv5s network, and the performance in terms of detection was barely affected. Figure 9 shows the results. Figure 9 shows four typical images in the results. The results based on the YOLOv5s algorithm had some false alarms and missed detections, which are marked by red circles in Figure 9. Our improved YOLOv5s algorithm greatly improved these issues, as shown in Figure 9. A large number of experiments have proven that our improved YOLOv5s algorithm has better performance in terms of detection.