## 4 Experiments, Results, and Discussion
### Experimental Results
#### 4.3.2 Rotationally Invariant Object Detection  
In this experiment, we used YOLOX-s as the baseline model and used the circular smooth label to calculate the angle loss to realize the rotation object detection of remote sensing images. Table 5 shows the detailed comparison between our method and the current common methods on the DOTA dataset. The accuracy of mAP reached 76.41%, even higher than the current SOTA algorithm ReDet [46]. The data in the table show that the precision of DCE-YOLOX is 0.16% higher than that of ReDet on the DOTA dataset. In addition, the memory occupied by our model weight is 68.5 MB, which is lower than all the algorithms in the table.  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Backbone** & **mAP** \\ \hline SSD300 & VGG16 & 10.9 \\ SSD512 & VGG16 & 20.8 \\ Faster R-CNN & ResNet101 & 60.5 \\ R-FCN & ResNet101 & 47.2 \\ YOLOv2 & Darknet19 & 21.4 \\ YOLOv3 & Darknet53 & 53.7 \\ YOLOv5s & CSPFocus & 71.0 \\ YOLOv5m & CSPFocus & 73.5 \\ YOLOv5l & CSPFocus & 74.4 \\ YOLOv5x & CSPFocus & 73.7 \\ DCE_YOLOX-x & CSPFocus & **83.9** \\ \hline \end{tabular}
\end{table}
Table 4: Detection results of different algorithms.  
Figure 7: The first 32 feature visualizations: (**a**) is before the improvement, and (**b**) is after the improvement.  
Figure 8 shows the confusion matrix of our method, and we can see the detection accuracy of each category. The best detection results are plane and ship, with an accuracy of 96%, and the worst category is soccer-ball-field, with only 52%. Because the detection of rotated objects is performed, there is a difference in the detection effect of some objects with similar shapes and sizes. Compared to horizontal detection, the angle prediction is added here. In addition, the number of objects in different categories in the dataset varies, which is one of the reasons for the difference in the detection results of different categories of objects. For specific category information and examples, please refer to the relevant documents of the DOTA dataset. It can be seen from the results that our algorithm has a good detection effect on small objects and objects with obvious features in remote sensing images, while the detection of other objects still needs to be improved.  
Table 6 shows the performance comparison results of the HRSC2016 dataset. The detection accuracy of our method is higher than that of other rotationally invariant object detection methods. The detection accuracy reaches 95.9%, and there is almost no false detection or missing detection. At the same time, it has a high detection speed. This shows that our method has a good detection effect in single-category or multi-category detection, which reflects the universality of DCE_YOLOX.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **mAP** & **Memory** \\ \hline R3 Det [47] & ResNet50 & 70.08 & 143 \\ ReDet & ResNet50 & 76.25 & 125 \\ PolarDet [48] & ResNet50 & 75.02 & 150 \\ S2 ANet [49] & ResNet50 & 74.12 & 148 \\ CFA [50] & ResNet50 & 73.45 & 141 \\ Oriented Reppoints [51] & ResNet50 & 75.97 & 230.5 \\ Oriented R-CNN [52] & ResNet50 & 75.87 & 158 \\ RepVGG-YOLO [53] & RepVGG & 74.13 & - \\ DCE-YOLOX & CSPFocus & 76.41 & 68.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection results on DOTA dataset.  
Figure 8: Confusion matrix of DCE_YOLOX.  
Figure 9 shows the visualization results of our method on the DOTA dataset and HRSC2016 dataset. The first three lines are the visualization results of the DOTA dataset, and the fourth line is the visualization results of the HRSC2016 dataset. It can be seen from the figure that our method has a good detection effect, even for dense and small objects.