## 4 Experiments and Discussion
### Experiment Results and Discussion in DIOR Dataset  
#### 4.4.1 Ablation Experiment  
Ablation experiments were conducted to verify the effectiveness of each improved module in YOLO-DSD, and the results are shown in Table 2. The detector improved with the DenseRes Block reduces Params by 23.9% (\(\frac{48.81-64.17}{64.17}\times\) 100%) and Flops by 29.7% (\(\frac{21.12-30.07}{30.07}\times\) 100%), and increases FPS by 63.4% (\(\frac{65.74-64.02}{40.2}\times\) 100%), while achieving a 0.2% higher mAP\({}^{0.5}\) and almost the same mAP\({}^{0.50.95}\) compared with YOLOv4 as the baseline. The detector improved by S-CBL\(\times\)5 in the neck based on \"+DenseRes Block\" is beneficial for mAP\({}^{0.5}\) and mAP\({}^{0.50.95}\), which are brought about by the increase in mAP\({}^{\text{M}}\) and mAP\({}^{\text{L}}\) without affecting the deployability and inference speed. However, the mAP\({}^{\text{S}}\) slightly decreased by 0.3% because the short-cut utilized in S-CBL\(\times\)5 strengthened the transmitting of the feature, and thus introduced background features additionally, which attenuated the representation of the feature for small-sized objects. The detector further improved by the DCA Block achieved a significant increase in mAP due to the enhancement of feature expression, and made up for the loss of mAP\({}^{\text{S}}\) caused by the short-cut with the same Params and Flops, while the FPS was only slightly reduced by 5.3 img/s. In summary, YOLO-DSD outperforms YOLOv4 both in the detection accuracy, deployability and speed evaluation indicator. YOLO-DSD based on YOLOv4 increases the commonly used indicator mAP\({}^{0.5}\) by 1.7% and the more rigorous indicator mAP\({}^{0.50.95}\) by 0.9%. Specifically, YOLO-DSD has a greater advantage in mAP\({}^{\text{M}}\) and mAP\({}^{\text{L}}\), while it achieves a similar and competitive mAP\({}^{\text{S}}\) compared with YOLOv4. In terms of deployability performance, the Params and Flops of YOLO-DSD decreased by 23.9% and 29.7% more than those of YOLOv4, respectively. YOLO-DSD also performs well in inference speed: it is 50.2% faster than YOLOv4 in FPS. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Detectors** & **Params** & **Flops** & **FPS** & **mAP\({}^{0.5}\)** & **mAP\({}^{0.50.95}\)** & **mAP\({}^{\text{S}}\)** & **mAP\({}^{\text{M}}\)** & **mAP\({}^{\text{L}}\)** \\ \hline YOLOv4(Baseline) & 64.17 M & 30.07 G & 40.2 & 71.3 & 39.1 & 10.1 & 30.2 & 55.1 \\ +DenseRes Block & 48.81 M & 21.12 G & 65.7 & 71.5 & 38.8 & 9.4 & 30.4 & 54.9 \\ +S-CBL\(\times\)5 & 48.81 M & 21.12 G & 65.7 & 71.9 & 39.2 & 9.1 & 30.9 & 55.7 \\ +DCA(YOLO-DSD) & 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 2: The ablation results of YOLO-DSD. Figure 12: The relationship between learning rate and epoch. We further analyzed the performance of the DenseRes Block. The ablation results of the DenseRes Block are shown in Table 3. The structure of the DenseRes Block in each detector is shown in Figure 13. Model 1 is the detector improved by the DenseRes Block without the structure of the 'Short-cut' and 'Combine'. 'Short-cut' and 'Combine' are introduced to the DenseRes Block in Model 2 and Model 3, respectively. Model 4 utilizes the complete DenseRes Block to improve the backbone of YOLOv4. From the comparison between Model 1 and Model 2, the 'Short-cut' introduced to DenseRes Block for the mitigation of feature loss can improve the mAP of objects in each size. After adding the 'Combine' to DenseRes Block, Model 3 performs better on the middle and large-sized object, while the mAP\({}^{\text{S}}\) decreases slightly by 0.1%. The possible reason for this is that the feature of the middle and large-sized object is obvious enough to build high-level semantic relevance with the background feature, while the feature of the small object is not obvious enough and thus it is easy for it to be overwhelmed.