## 4 Experiments and Discussion
### Experiment Results and Discussion in DIOR Dataset
#### 4.4.2 Comparative Experiment
Although the Params and Flops of CenterNet are 67% and 69% that of YOLO-DSD, and the FPS is 46% faster, the detection accuracy of CenterNet is significantly lower than that of YOLO-DSD (mAP\({}^{0.5,0.95}\);35.8% vs. 40.0%), and the mAP\({}^{\text{S}}\) is only 62.5% that of YOLO-DSD. YOLO-Lite has an obvious disadvantage in detection accuracy for small and large-sized objects, even though it has a better deployability compared with YOLO-DSD. The inference speed of YOLOv3 is nearly the same as that of YOLO-DSD, but the deployability and detection accuracy of YOLOv3 are obviously inferior to that of YOLO-DSD. The superiority of YOLO-DSD compared with YOLOv4 was analyzed and proved in ablation experiments. Therefore, YOLO-DSD outperforms other SOTA detectors in the balance of accuracy, deployability and speed. Figures 15-17 exhibit the detection performance of Faster-RCNN, CenterNet, YOLOv4 and YOLO-DSD on DIOR. The detection result of the small-sized instance in Figure 15 indicates that both Faster-RCNN and CenterNet obviously miss detection. Although YOLOv4 could completely detect airplanes, it incorrectly detected a storage tank. Our YOLO-DSD can correctly detect all airplanes without any false detection. Figure 16 presents the detection results of an instance in the complex urban background. We can see that Faster-RCNN only detects one ground track field, and that CenterNet misses two bridges and two ground track fields and misdetects an overpass. YOLOv4 misses one bridge and one ground track field, whereas YOLO-DSD detects all objects correctly. The detection results of instances in a complex suburban background are given in Figure 17. It can be seen that Faster-RCNN detects only one Expressway-Service-Area, CenterNet has two false detections of an overpass and windmill, YOLOv4 detects two Expressway-Service \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Attention Mechanism** & **Params** & **Flops** & **FPS** & **mAP\({}^{0.5}\)** & **mAP\({}^{0.50,95}\)** & **mAP\({}^{\text{S}}\)** & **mAP\({}^{\text{M}}\)** & **mAP\({}^{\text{L}}\)** \\ \hline None (Baseline) & 0 & 0 & 65.7 & 71.9 & 39.2 & 9.1 & 30.9 & 55.7 \\ CA & 42.36 K & 1126.41 K & 57.2 & 71.6 & 39.0 & 9.1 & 30.4 & 55.9 \\ CBAM & 102.79 K & 516.59 K & 52.8 & 70.6 & 38.1 & 8.4 & 29.3 & 55.7 \\ SE & 51.20 K & 51.272 K & 62.3 & 72.1 & 39.3 & 9.0 & 30.8 & 56.5 \\ ECA & 0.02 K & 0.02 K & 62.9 & 72.0 & 39.3 & 9.2 & 30.7 & 56.1 \\ DCA (R = 32) & 51.22 K & 830.24 K & 60.4 & 73.0 & 40.0 & 9.6 & 31.6 & 56.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of comparative experiment for different attention mechanisms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Detector** & **Params** & **Flops** & **FPS** & **mAP\({}^{0.5}\)** & **mAP\({}^{0.50,95}\)** & **mAP\({}^{\text{S}}\)** & **mAP\({}^{\text{M}}\)** & **mAP\({}^{\text{L}}\)** \\ \hline RetinaNet & 36.72 M & 17.24 G & 44.6 & 62.7 & 37.6 & 4.8 & 30.9 & 57.5 \\ EfficientDet & 3.60 M & 1.30 G & 18.1 & 50.4 & 29.4 & 2.4 & 24.9 & 46.0 \\ SSD & 26.15 M & 59.59 G & 87.3 & 61.9 & 37.8 & 4.6 & 31.0 & 58.2 \\ CenterNet & 32.67 M & 14.62 G & 88.5 & 61.4 & 35.8 & 6.0 & 27.3 & 55.3 \\ Faster-RCNN & 28.47 M & 364.14 G & 21.9 & 56.1 & 31.8 & 2.8 & 23.7 & 53.2 \\ YOLO-Lite & 10.48 M & 3.89 G & 54.1 & 64.5 & 33.1 & 6.5 & 26.1 & 48.7 \\ YOLOv3 & 61.63 M & 32.83 G & 61.4 & 69.2 & 34.7 & 7.8 & 27.4 & 49.8 \\ YOLOv4 & 64.17 M & 30.07 G & 40.2 & 71.3 & 39.1 & 10.1 & 30.2 & 55.1 \\ YOLO-DSD (ours) & 48.81 M & 21.12 G & 60.4 & 73.0 & 40.0 & 9.6 & 31.6 & 56.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of comparative experiment for different detectors. Areas as one, and YOLO-DSD correctly detects all objects. The above instances verify that YOLO-DSD can handle object detection under different complex backgrounds well. The precision-recall curves and AP (IOU = 0.5) of YOLOv4 and YOLO-DSD in each category are given in Figure 18 for a better illustration of the difference in detection accuracy.