## 4 Results and Discussion
### Ablation Experiment Results  
We performed several ablation tests to comprehensively evaluate the enhanced EYMR-Net algorithm's performance. During the training phase, we used the YOLOv7 model as the baseline. Throughout the process, each model was trained for 300 epochs using a batch size of 16. We introduced Swin Transformer (STR) modules and the CBAM attention mechanism to enhance the model's performance. Furthermore, we improved the bounding box regression mechanism of YOLOv7 to handle rotated boxes based on circular smooth labels. To assess the combined effects of the EYMR-Net variants, we conducted corresponding ablation experiments, and the results are shown in Table 2. It can be seen that the three proposed modules all play a role in detection accuracy and efficiency. The organic combination of STR, CBAM, and RBO makes the proposed EYMR-Net achieve better robustness to cope with remote sensing detection and identification tasks under multi-scale changes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **STR** & **CBAM** & **RBO** & **Precision** & **Recall** & **mAP0.5** \\ \hline YOLOv7 & & & & 74.32\% & 63.87\% & 69.5\% \\ YOLOv7\_O & & & \(\surd\) & 76.91\% & 64.16\% & 70.72\% \\ YOLOv7\_O\_SW & \(\surd\) & & \(\surd\) & 74.86\% & 70.46\% & 71.48\% \\ YOLOv7\_O\_CB & & \(\surd\) & \(\surd\) & 77.63\% & 71.07\% & 73.25\% \\ EYMR-Net & \(\surd\) & \(\surd\) & \(\surd\) & 79.76\% & 72.18\% & 74.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation studies on the DOTA dataset. Figure 8: Detection error distribution of different object types. Considering that our method is optimized on the advanced YOLOv7, we drew precision, recall, and mAP0.5 curves to visually compare the changes in EYMR-Net compared to the YOLOv7 series. As shown in Figure 9, the specific values of each item in the last round of the source code of YOLOv7 are 74.32%, 63.87%, and 70.72%, while the values indicating the performance of our EYMR-Net are 79.76%, 72.18%, and 74.30%. The comparison reveals that the EYMR-Net can reach the steady-state value faster during the entire learning process, with better convergence and accuracy, outperforming YOLOv7 series approaches. To test the detection ability of EYMR-Net in real remote sensing scenes, five representative image samples are selected for visualization and comparison in this experiment: group 1 includes a dense and oriented large vehicle (LV) in a parking lot, group 2 comprises airplanes (PLs) and helicopters (HCs) in an airport scene, group 3 includes a small vehicle (SV) with small targets in a parking lot scene, group 4 comprises a coastal scene with multiple similar features of multiple ships (SHs) and harbors (HBs), and group 5 represents multiple categories simultaneously appearing in the remote sensing complex scene of tennis courts (TCs), basketball courts (BCs), and small vehicles (SVs). The confidence threshold is set to 0.25, and the detection results are shown in Figure 10.  
A thermal effect map is plotted based on Grad-cam to further reflect the feature information extraction capability of the detector, as shown in Figure 11. In both figures, the first column comprises the original pictures, the second column lists the detection results of YOLOv7 after adding the Swin Transformer module (STR), and the third column lists the results of YOLOv7 after adding the Rotation Detection Frame (RBO). The last column presents the detection results obtained using our EYMR-Net. Upon comparison, it was found that the improved algorithm in this paper dramatically improves the detection of multiple categories of targets in remote sensing scenes, such as large vehicles (LVs), airplanes (PLs), and helicopters (HCs), in the images of groups 1 and 2.