## 4 Experiments and Discussion
### Experiment Results and Discussion in DIOR Dataset
#### 4.4.2 Comparative Experiment  
Four experiments were conducted in this study to verify the superiority of the proposed method. (1) ResNet50 [34], VGG16 [33] and the backbones that were established based on the CSP DarkNet framework with different feature extraction modules, including the Res Block [34], ResNeXt Block [35], Res2 Block [36], Dense Block [27], CSP Block [37] and DenseRes Block, were compared. (2) A comparison of different neck structures, including FPN [16], BFPN [40], PANet [41], S-PANet (PANet improved with the proposed S-CBL\(\times\)5) and none (without feature pyramid structure), was conducted. (3) The performance of different attention mechanisms, including the SE Block [42], ECA Block [43], CA Block [44], CBAM Block [45] and DCA Block(R = 32), was compared and analyzed. (4) YOLO-DSD was compared with eight SOTA detectors, including Faster-RCNN, SSD, RetinaNet, YOLOv3, YOLOv4, YOLO-Lite (MobileNetV2 [51]--YOLOv4), CenterNet [7] and EfficientDet [9], which have been widely applied in various natural scene visual detection tasks due to their acceptable tradeoff between accuracy, deployability and inference time. Comparative experiment for different backbones: The performances of the CSP DarkNet, which is improved by the proposed DenseRes Block (DarkNet-DenseRes) and other backbones, are demonstrated in Table 6. Based on the CSP DarkNet framework, the proposed DenseRes Block outperforms the ResNeXt Block and Dense Block in all indicators. Although the mAP\({}^{0.5}\) and mAP\({}^{0.50,95}\) of DarkNet-DenseRes are slightly lower than those of DarkNet-Res by 0.1% and 1.3%, the Params and Flops of DarkNet-DenseRes are only approximately 1/3 and 1/4 of DarkNet-Res, while the FPS of DarkNet-DenseRes is approximately 1/4 higher than that of DarkNet-Res. Similarly, the mAP\({}^{0.5}\) and mAP\({}^{0.50,95}\) of DarkNet-DenseRes are 0.9% and 1.1% lower than those of DarkNet-Res2; however, the Params and Flops of DarkNet-DenseRes are only approximately 1/3 and 1/2 of those of DarkNet-Res2, while the interference speed is 2.3 times that of DarkNet-Res2 according to FPS. The superiority of DarkNet-DenseRes compared with CSP DarkNet was analyzed and \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Detectors**} & \multicolumn{2}{c}{**DCA Block**} & \multirow{2}{*}{**FPS**} & \multirow{2}{*}{**mAP\({}^{0.50,95}\)**} & \multirow{2}{*}{**mAP\({}^{0.50,95}\)**} & \multirow{2}{*}{**mAP\({}^{\text{S}}\)**} & \multirow{2}{*}{**mAP\({}^{\text{M}}\)**} & \multirow{2}{*}{**mAP\({}^{\text{L}}\)**} \\ \cline{2-2} \cline{6-8}  & **Zoom Factor ‘R’** & & & & & & \\ \hline Model 1 & R = 1 & 60.3 & 72.4 & 39.3 & 9.2 & 31.1 & 55.8 \\ Model 2 & R = 2 & 60.3 & 72.2 & 39.2 & 9.1 & 30.3 & 55.9 \\ Model 3 & R = 4 & 60.3 & 71.8 & 39.0 & 9.2 & 30.1 & 55.9 \\ Model 4 & R = 8 & 60.4 & 72.8 & 39.8 & 9.7 & 31.1 & 56.4 \\ Model 5 & R = 16 & 60.4 & 72.3 & 39.5 & 9.4 & 31.2 & 56.2 \\ Model 6 & R = 32 & 60.4 & 73.0 & 40.0 & 9.6 & 31.6 & 56.4 \\ Model 7 & R = 64 & 60.4 & 72.1 & 39.6 & 9.2 & 31.4 & 56.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different zoom factor ’R’s in DCA Block. Figure 14: The structure of DCA Block in each detector in Table 5.  
proved in ablation experiments. DarkNet-DenseRes also has obvious advantage in all indicators compared with ResNet50. Although DarkNet-DenseRes has a similar accuracy and speed to VGG16, VGG16 has seven times as much Flops than that of DarkNet-DenseRes. Therefore, DarkNet-DenseRes achieves the optimal balance of accuracy, deployability and speed. Comparative experiment for different necks: Table 7 shows the performance of each neck structure that was tested by applying a no-feature pyramid structure (None), FPN, BFPN, PANet (Baseline) and S-PANet to the modified YOLOv4, with the DenseRes Block in the backbone.