## 4 Experiments and Discussion
### Experiment Results and Discussion in DIOR Dataset
#### 4.4.2 Comparative Experiment
'None' has the lowest Params (18.83 M) and Flops (4.89 G) and the highest FPS (85.5 img/s), but it does not perform well in detection accuracy, and, in particular, its mAP\({}^{\text{S}}\) is only 8.1%, whereas that of the other four necks ranges from 9.1% to 9.5%. Therefore, the feature pyramid structure is vital for detection accuracy and, in particular, for small size objects, which occupy more than 50% in DIOR. Although FPN and BFPN are slightly better than PANet in deployability and inference speed, they have more than a 2.6% inferiority in mAP of middle and large-sized objects, which, in total, account for approximately 50% of objects in DIOR. It was proven that the structure of PANet is important to the detection accuracy in YOLOv4 for ORSIs. PANet and S-PANet have almost the same Params, Flops and FPS, but our S-PANet performs better than PANet in mAP\({}^{0.5}\) and mAP\({}^{0.5,0.95}\). In conclusion, S-PANet is more suitable for optical remote sensing object detection than other necks. Comparative experiments for different attention mechanisms: Taking modified YOLOv4 with the DenseRes Block in the backbone and S-PANet in the neck as the baseline (None), the indicator values of different attention mechanisms are exhibited and compared in Table 8. The CA Block and CBAM Block containing the spatial attention mechanism fail to improve the detection accuracy, and the FPS decreases significantly due to those complex structures. Most channel attention mechanisms, including the SE Block, ECA Block and DCA Block, can improve the detection accuracy. The DCA Block improves the detection accuracy for small, medium and large sizes of objects, and achieves the highest mAP\({}^{0.5}\) = 73.0% and mAP\({}^{0.5,0.95}\) = 40.0%, with an increase of 1.1% and 0.8% compared with 'None', respectively, when R = 32, and the FPS only decreases by 5.3 img/s. In the case of the SE Block, mAP\({}^{0.5}\) and mAP\({}^{0.5,0.95}\) increases by 0.2% and 0.1%, and the FPS decreases by 3.4 img/s. The ECA Block improves both mAP\({}^{0.5}\) and mAP\({}^{0.5,0.95}\) by 0.1%, and decreases \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Backbone** & **Params** & **Flops** & **FPS** & **mAP\({}^{0.5}\)** & **mAP\({}^{0.50,95}\)** & **mAP\({}^{\text{S}}\)** & **mAP\({}^{\text{M}}\)** & **mAP\({}^{\text{L}}\)** \\ \hline CSP DarkNet (Baseline) & 26.61 M & 17.34 G & 40.2 & 71.3 & 39.1 & 10.1 & 30.2 & 55.1 \\ DarkNet-Res 1 & 40.58 M & 24.61 G & 52.8 & 71.6 & 40.1 & 9.9 & 31.7 & 56.1 \\ DarkNet-ResNeXt 2 & 20.55 M & 12.71 G & 39.1 & 68.4 & 36.4 & 8.2 & 28.3 & 52.6 \\ DarkNet-Res2 3 & 31.65 M & 19.33 G & 28.4 & 72.4 & 39.9 & 10.2 & 31.6 & 55.4 \\ DarkNet-Dense 4 & 14.06 M & 8.16 G & 50.9 & 69.6 & 37.5 & 7.8 & 29.7 & 54.5 \\ DarkNet-Dense Res 5(Ours) & 11.26 M & 8.42 G & 65.7 & 71.5 & 38.8 & 9.4 & 30.4 & 54.9 \\ ResNet50 & 23.51 M & 13.41 G & 47.4 & 68.5 & 36.5 & 7.8 & 28.7 & 53.2 \\ VGG16 & 17.07 M & 54.64 G & 70.1 & 71.1 & 38.9 & 9.9 & 29.8 & 55.2 \\ \hline \hline \end{tabular} Note: \({}^{1,2,3,4,5}\) means CSP DarkNet utilizes Res Block, ResNeXt Block, Res2 Block, Dense Block and DenseRes Block as the main feature extraction module, respectively.  
\end{table} 
Table 6: Results of comparative experiment for different backbones. the FPS by 2.8 img/s. Therefore, the proposed DCA Block can achieve the best balance between accuracy and speed. Comparative experiments for different detectors: The performances of the proposed YOLO-DSD and eight SOTA detectors are demonstrated in Table 9. RetinaNet and EfficientDet have a better deployability than YOLO-DSD, but their detection accuracy, especially for small-sized objects and speed, are far behind that of YOLO-DSD, so this hinders the application of these detectors in optical remote sensing object detection. The large Flops of SSD and Faster-RCNN require a huge amount of computing resources, which greatly increases the difficulty in deploying them on edge devices.