# UAV Image Small Object Detection Based on RSAD Algorithm
## 4 Results
### Ablation Experiments  
In order to verify the effectiveness of the improvement of each unit, a series of ablation experiments were conducted on the test set in this study. The mAP, the number of parameters, and the FPS (3060 Ti) were selected as the evaluation metrics, and the results of the experiments are shown in Table 2. The results of the ablation experiments show that after adding the SAFM feature fusion, the mAP improvement rate is the largest, and the improvement rate reaches 7.4%, which shows from the side that the SAFM module obtains global weighted feature mapping by reshaping and concatenating the features of the backbone network, which efficiently and selectively organically fuses the high-level semantic information in the deep layer and the geometric information in the shallow layer together, and the feature layer used for detection contains rich semantic and geometric information at the same time, which helps the model to better detect small objects in UAV images.  
The increase in accuracy also brings about an increase in the number of parameters and a slight decrease in FPS, but it also meets the demand for real-time performance. The application of the new backbone network and the introduction of the Focal Loss also have some improvement, with an improvement rate of 1.3% and 1.9%, respectively. The improvement in detection accuracy reflects that the feature extraction ability of ResNet-50 is better than that of VGG-16, and the loss function of Focal Loss increases the model's focus on small objects in UAV images. Although the model's parameters are improved by 14.5B, the FPS is not substantially reduced compared to the original SSD algorithm.  
In Figure 8 the mAP curves during training are shown. The AP curve of the model rises rapidly between 0 and 20,000 iterations and converges by the time it reaches 100,000 iterations. From the figure, it can be seen that the method in this study clearly outperforms SSD.When replacing the backbone network with ResNet-50, it reduces the convergence speed of the model, but this effect is eliminated after adding SAFM and Focal Loss. Moreover, SAFM improves the model mAP significantly, proving that the SAFM module fusion strategy proposed in this study is feasible. After the introduction of Focal Loss, no obvious enhancement effect can be seen between 0 and 60,000 iterations, and the effect of Focal Loss is demonstrated after 60,000 iterations.  
In order to validate the feature extraction capability of this study's model, the visualized feature heatmaps of this study's model and the SSD model were compared. The results are shown in Figure 9, from which it can be seen that before adding SAFM, the model extracted the feature information more roughly and there was a lot of background noise information. After adding SAFM, it can be clearly seen that the model extracts the features more accurately and eliminates a lot of background and noise information.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**VGG-16** & **ResNet-50** & **SAFM** & **Focal Loss** & **mAP/\%** & **Parameters/B** & **FPS (3060 Ti)** \\ \hline \(\blacktriangledown\) & & & & 19.9 & 22.9 & 55 \\  & \(\blacktriangledown\) & & & 21.2 & 29.8 & 59 \\  & \(\blacktriangledown\) & \(\blacktriangledown\) & & 28.6 & 37.4 & 52 \\  & \(\blacktriangledown\) & \(\blacktriangledown\) & \(\blacktriangledown\) & 30.5 & 37.4 & 52 \\ \hline \end{tabular}
\end{table}
Table 2: Ablation experiment results.