## 4 Results and Discussion
### Ablation Experiment Results
If these categories were detected using the original algorithm, YOLOv7, they would contain a large number of redundant pixels of the background in the detection box, and the IOU between neighboring detection boxes would be too large, which would prevent the precise location of the position from being found and prevent the contour of a single target. On the contrary, EYMR-Net, presented in this paper, adopts a rotating box annotation that can perfectly fit the object's shape, and the IOU between adjacent detection boxes is much lower than that of YOLOv7. Densely arranged targets with considerable overlaps are best located using the rotating box. The improved multi-scale rotating target detector, EYMR-Net, presented in this paper solves the problem of excessive redundant pixels in the detection box brought about by detection with a horizontal box, which realizes target detection that better fits the shape of the object and improves the detection effect of the densely arranged targets in remote sensing scenes. For example, for the harbors (HAs) and ships (SHs) in the picture in group 4, the horizontal box of the original algorithm, YOLOv7, cannot accurately delineate the area of the ships moored near the harbor, which is not conducive to the detection of ship targets  
Figure 9: Results of curve analysis. in marine remote sensing scenes. For the very small target ships moored on the shore in the lower-right bit of the picture, the improved algorithm presented this paper detects 16 ships, while the original algorithm YOLOv7 detects 14 ships, which proves that the new feature extraction networks backbone and neck, constructed using this paper's algorithm based on the attention of the Swin Transformer and the CBAM, effectively optimizes the small-scale target leakage detection problem under complex remote sensing scenes. For the remote sensing images in group 5, due to the overly complex background of urban remote sensing images, there are different categories of multiple scales: tennis courts (TCs), basketball courts (BCs), and small vehicles (SVs). The large-scale targets in the remote sensing scene, the tennis courts, and the basketball courts are in the form of angular rectangles. The original algorithm does not have a good detection effect for face-to-face directional alignment, there are too many redundant pixels in the background, the overlap of the neighboring frames is more than 50%, and the improved EYMR-Net improves the problem efficiently and superiorly differentiates the contour areas of different targets. Figure 10: Remote sensing detection results of EYMR-Net vs. YOLOv7 series. For further visual analysis, we utilized a confusion matrix to evaluate the accuracy of the EYMR-Net model's predictions. This matrix's vertical axis represents the actual target categories, while the horizontal axis represents the predicted categories. Figure 12 provides a visual representation of this matrix. Our analysis of the confusion matrix revealed that the proposed EYMR-Net model achieved high accuracy rates for small vehicles, large vehicles, planes, ships, harbors, tennis courts, and basketball courts, with correct prediction rates of 71%, 86%, 93%, 88%, 86%, 93%, and 71%, respectively. These results demonstrate the model's ability to detect targets of various scales accurately, with a particularly impressive performance in detecting small objects. Figure 11: Detection and thermal diagrams of EYMR-Net vs. YOLOv7 series.