## 4 Experimental Results and Discussions
### Experimental Results
#### 4.3.2 Ablation Study on Pascal VOC 2007  
In order to test the effects of the feature fusion module and the attention mechanism module, we ran models with different modules added to the SSD7 on the Pascal VOC 2007 test set and discussed each method's results. All experimental settings were consistent, except for the network structure and certain components.  
First, we tested the effect of the attention mechanism module. Table 2 shows that adding the attention mechanism to different networks can get different results. Comparing the original SSD7 (row 1) and adding the attention module on SSD7 (row 2), using the attention mechanism on SSD7 can get 37.6% mAP while SSD7 can only achieve 36.6%.  
Figure 7: Qualitative detection examples on the NWPU VHR-10 dataset with the proposed SSD7-FFAM (83.7% mean Average Precision (mAP)). For each pair, the left (**a**,**c**,**e**) is the original image and right (**b**,**d**,**f**) is the result of the SSD7-FFAM. Each color corresponds to an object category in that image.  
At the same time, we compared the number of parameters and find that the attention mechanism only increases by 0.01 MB on average. In the following experiments, we used the attention mechanism to obtain better detection results.  
Then, we conducted comparative experiments to test the effects of the feature fusion module on our experimental platform. The results are also listed in Table 2. The SSD7-FFAM used the feature fusion module introduced in Section 3, while the FPSSD7 [45] used bilinear interpolation for upsampling during feature fusion. The mAP has an improvement of 7.6% using the proposed feature fusion method (row 4) compared to the method using FPSSD7 [45] is only 5.1% (row 3). The results demonstrate that using the feature fusion module with deconvolution contributes more to the detection accuracy than the bilinear interpolation. Compared with the FPSSD7 [45], the combination of FPSSD7 [45] and attention mechanism results in a higher mAP with 44.2% (row 5). The results show that the SSD7-FFAM (row 6) we proposed has an 8.1% improvement compared to the original SSD7.  
Table 3 shows that the proposed SSD7-FFAM achieves the second-lowest average running time among the six different models, only 0.145 s slower than FPSSD7 [45], while the accuracy of SSD-FFAM is 3% higher. The average running time of SSD7-FFAM in comparison to other detectors is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Feature Fusion** & **Deconv** &
\begin{tabular}{c} **Parameters** \\ **(MB)** \\ \end{tabular} & **mAP (\%)** \\ \hline SSD7 [33] & \(\times\) & \(\times\) & 1.21 & 36.6 \\ SSD7 + AM & \(\times\) & \(\times\) & 1.22 & 37.6 \\ FPSSD7 [45] & Ele-sum & \(\times\) & 1.54 & 41.7 \\ SSD7 + FF & Ele-sum & \(\surd\) & 1.92 & 44.2 \\ FPSSD7 + AM & Ele-sum & \(\times\) & 1.55 & 44.2 \\ SSD7-FFAM & Ele-sum & \(\surd\) & 1.93 & **44.7** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of the feature fusion module and the attention mechanism module on the SSD7. FF indicates feature fusion module; AM indicates attention mechanism module, Deconv indicates deconvolution module.  
Figure 8: Accuracy and average running time comparison of the proposed SSD7-FFAM with other methods on the Pascal VOC 2007 test set. SSD7-FFAM is more accurate compared with other models with little compromise on speed.  
Therefore, the quantitative results in Tables 2 and 3 show that the FFAM has a good effect on improving detection accuracy and can more accurately detect various objects in the dataset.