## 4 Experiment and Analysis
In the MAR20 dataset, the specific aircraft models referred to as A1-A20 are listed as shown in Table 5.  
This test verified the algorithm through 20 different types of accuracy in the statistical data set and compared it with other classical algorithms as Table 6:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Our** & \multirow{2}{*}{**FCOS**} & \multirow{2}{*}{**ATSS**} & **Faster** & \multirow{2}{*}{**TSD**} & **Cascade** & **Double-** \\  & & & & & **R-CNN** & & **R-CNN** & **Head** \\ \hline \multicolumn{8}{c}{**One-Stage Network**} & \multicolumn{8}{c}{**Two-Stage Network**} \\ \hline A1 & 84.9 & 71.1 & 69.9 & 79.7 & **87.2** & 79.6 & 82.3 \\ A2 & **96.2** & 79.4 & 83.7 & 81.6 & 86.7 & 81.4 & 81.2 \\ A3 & **96.0** & 67. In summary, through the visual experiments in Figure 10, we have not only verified the superiority of our algorithm but also provided thought-provoking insights for future fine-grained detection. There are some shortcomings in Figure 10f-j that require further improvement. The specific analysis is as follows:  
In Figure 10, there is a phenomenon where A1 was incorrectly recognized as A19. However, considering the similar morphology of A1 and A19 themselves and the low \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Our** & \multirow{2}{*}{**FCOS**} & \multirow{2}{*}{**ATSS**} & **Faster** & \multirow{2}{*}{**TSD**} & **Cascade** & **Double-** \\  & **Paper** & & & & & **R-CNN** & **Head** \\ \hline \hline \multicolumn{8}{c}{**One-Stage Network**} & \multicolumn{8}{c}{**Two-Stage Network**} \\ \hline A17 & **97.2** & 87.1 & 89.5 & 90.2 & 95.5 & 90.1 & 89.8 \\ A18 & **82.3** & 38.6 & 36.1 & 54.2 & 58.5 & 42.6 & 44.1 \\ A19 & **80.6** & 69.2 & 67.7 & 76.6 & 79.6 & 78.3 & 74.4 \\ A20 & **86.5** & 66.4 & 77.5 & 78.6 & 80.1 & 76.7 & 77.2 \\ mAP & **89.3** & 68.8 & 72.2 & 79.6 & 82.7 & 78.0 & 77.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 10: Visualization results of the MAR20 dataset. The results from part of the test set of the Mar20 dataset are shown in (**a-j**). resolution of the image, this error can be explained. Nevertheless, it suggests room for improvement through a combination of super-resolution and recognition. Both Figure 10f,j exhibit cases of missed detection. Upon analyzing the specific reasons, it was found that the majority of missed models were A5. By comparing the dataset, it can be observed that the training data for A5 are relatively limited. This issue can be effectively addressed by expanding the database. Figure 10g,i have reasons for incorrect recognition, especially in the case of i, where traces of aircraft having stopped were recognized as aircraft. Apart from resolution, the contrast between the recognition target and its surroundings also plays a role. This provides valuable background support for future research. However, it is worth noting that the cases listed from a to e demonstrate accurate recognition without omissions. This portion serves as evidence of the superiority of our algorithm in fine-grained detection. Moreover, even in the case of profiles of aircraft that are prone to erroneous recognition, like the one in Figure e, no significant interference with aircraft recognition occurred when there was an adequate amount of training data. Based on these findings, our algorithm shows promising practical applications in the field of engineering for fine-grained detection.