## 4 Results and Discussion
### Comparative Experimental Results  
To further validate the effectiveness of the enhanced EYMR-Net algorithm, we choose five classical target detection algorithms and this paper's algorithm for comparison. As shown in Table 1, a comparison of the experimental results of the proposed EYMR-Net with other state-of-the-art object detection models is presented with the DOTA dataset. In the remote sensing image detection task, the evaluation metrics of AP 0.5(%) and mAP 0.5(%) are chosen to evaluate the recognition performance on the remote sensing targets of each model. It shows that the EYMR-Net obtains a higher mAP0.5, which proves that the optimization approaches are effective. In detail, the proposed EYMR-Net achieves an mAP0.5 of 74.30%, outperforming CenterNet, YOLOv5, RetinaNet, Faster R-CNN, and CornerNet. Among them, our model achieves the best accuracy of AP0.5 for ships (SHs), small vehicles (SVs), and large vehicles (LVs), which are small-scale dense targets with an optimal accuracy of AP0.5, reaching 89.9%, 69.0%, and 87.8%, respectively. This demonstrates that our detector, EYMR-Net, greatly improves the detection of small targets compared to classical target detectors in the complex context of remotely sensed images. It also achieves quite excellent detection metrics in several categories, such as airplanes (PLs) and harbors (HAs). In order to further illustrate the advantages, we draw the detection performance results of the proposed methods including CornerNet, YOLOv5, RetinaNet, Faster R-CNN, and CenterNet, as shown in Figure 6.  
At the same time, we conduct tests on different detectors: for group 1, we choose remote sensing images of large vehicles (LVs) and small vehicles (SVs) in a parking lot in a small-scale dense and oriented row; for group 2, we choose remote sensing images of a ship (SH), small vehicles (SVs), and a harbor (HA) in a complex background simultaneously; for group 3, we choose remote sensing images of small and large vehicles at the same time; for group 4, we select remote sensing images of helicopters (HCs) and passenger planes \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **PL** & **BD** & **BR** & **GTF** & **SV** & **LV** & **SH** & **TC** & **BC** & **ST** & **SBF** & **RA** & **HA** & **SP** & **HC** & **mAP** \\ \hline CornerNet & 67.8 & 78.9 & 53.6 & 68.1 & 31.4 & 46.5 & 27.1 & 88.0 & 63.8 & 53.6 & 62.5 & 69.8 & 58.8 & 43.1 & 23.9 & 55.78 \\ Faster & 76.2 & 63.7 & 29.6 & 54.9 & 67.7 & 62.6 & 67.7 & 86.9 & 50.1 & 67.3 & 55.8 & 40.9 & 65.7 & 43.6 & 48.3 & 56.13 \\ RSD & 79.4 & 77.1 & 17.7 & 64.0 & 35.3 & 38.0 & 37.2 & 89.4 & 69.6 & 59.3 & 50.3 & 52.9 & 47.9 & 47.4 & 46.3 & 54.10 \\ RetinaNet & 73.0 & 68.2 & 66.0 & 76.2 & 25.5 & 62.8 & 68.6 & 84.2 & 62.3 & 51.3 & 57.8 & 80.9 & 66.0 & 42.7 & 48.5 & 63.27 \\ YOLOv5 & 89.9 & 73.2 & 44.2 & 47.2 & 62.2 & 82.3 & 87.0 & 93.1 & 64.1 & 69.4 & 46.0 & 56.8 & 75.6 & 60.2 & 67.1 & 67.90 \\ CenterNet & 97.3 & 78.6 & 49.4 & 53.4 & 62.9 & 80.2 & 89.2 & 94.6 & 66.1 & 85.8 & 64.9 & 69.0 & 78.9 & 75.6 & 66.8 & 73.94 \\ EYMR-Net & 94.2 & 81.9 & 53.6 & 67.8 & 69.0 & 87.8 & 89.9 & 94.2 & 71.7 & 73.8 & 71.5 & 55.8 & 84.7 & 65.1 & 51.2 & 74.30 \\ (Ours) & & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of the detecting results with the DOTA dataset. (PLs) in an airport scene; and for group 5, we select remote sensing images of harbors (HAs), ships (SHs), and vehicles on shore in a complex scene. The test samples are shown in Figure 7. Faster R-CNN, SSD, RetinaNet, and YOLOv5 all have different degrees of small target leakage detection problems, such as large vehicles and small vehicles appearing at the same time in group 1 of images, and small vehicles (SVs) in the upper left corner of the image, and only our detector EYMR-Net detected five of them. The red circles in the figure represent the leakage detection areas of different models in small object detection.