## 4 Experimental Results and Discussions
### Experimental Results
#### 4.3.3 Extended Research on SSD300  
To further verify the effectiveness of the proposed FFAM method, in this experiment, we also added it to the original SSD [4], which is called SSD300-FFAM, and compared it with other state-of-the-art detectors on the Pascal VOC 2007 test set. Except for the different sizes of detection feature maps, other experimental settings are consistent with SSD300 [4]. We first compared the proposed SSD300-FFAM with other state-of-the-art detectors on the Pascal VOC 2007 test set. All detectors used VGG-16 as the backbone network, and their input image size was 300 \(\times\) 300. Since our methods are mainly modified based on SSD300 [4], we chose SSD300 [4] as the baseline method. The detection results are summarized in Table 4. The results show that SSD300-FFAM achieves 78.7% mAP, which has an improvement of 1.2% over the baseline method SSD300 and is 5.5% higher than the Faster R-CNN. SSD300-FFAM is slightly better than SSD-TSEFFM300 [46] (78.7% vs. 78.6%). Compared to other detectors, we also achieved much better performance. SSD300-FFAM shows a great improvement in testing tasks with specific backgrounds and some small objects, e.g., airplane (82.6%), chair (61.7%), motorbike (86.3%), and train (87.8%). This shows that SSD300-FFAM improves the detection effect of SSD on small objects to a certain extent. Therefore, the proposed feature fusion and attention mechanism method plays a useful role in object detection. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **mAP (\%)** & **Aero** & **Bike** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** \\ \hline R-CNN [47] & 50.2 & 67.1 & 64.1 & 46.7 & 32.0 & 30.5 & 56.4 & 57.2 \\ Fast R-CNN [28] & 70.0 & 77.0 & 78.1 & 69.3 & 59.4 & 38.3 & 81.6 & 78.6 \\ Faster R-CNN [6] & 73.2 & 76.5 & 79.0 & 70.9 & 65.5 & 52.1 & 83.1 & 84.7 \\ ION [23] & 76.5 & 79.2 & 79.2 & 77.4 & 69.8 & 55.7 & 85.2 & 84.2 \\ MR-CNN [48] & 78.2 & 80.3 & 84.1 & 78.5 & 70.8 & 68.5 & **88.0** & 85.9 \\ SSD300 [4] & 77.5 & 79.5 & 83.9 & 76.0 & 69.6 & 50.5 & 87.0 & 85.7 \\ RON [21] & 76.6 & 79.4 & 84.3 & 75.5 & 69.5 & **56.9** & 83.7 & 84.0 \\ SSD-TSEFFM300 [46] & 78.6 & 81.6 & **94.6** & **79.1** & **72.1** & 50.2 & 86.4 & **86.9** \\ SSD300-FFAM & **78.7** & **82.6** & 86.8 & 78.5 & 70.3 & 55.9 & 86.0 & 86.5 \\ \hline
**Methods** & **mAP (\%)** & **Cat** & **Chair** & **Cow** & **Table** & **Dog** & **Horse** & **Mbike** \\ \hline R-CNN [47] & 50.2 & 65.9 & 27.0 & 47.3 & 40.9 & 66.6 & 57.8 & 65.9 \\ Fast R-CNN [28] & 70.0 & 86.7 & 42.8 & 78.8 & 68.9 & 84.7 & 82.0 & 76.6 \\ Faster R-CNN [6] & 73.2 & 86.4 & 52.0 & 81.9 & 65.7 & 84.8 & 84.6 & 77.5 \\ ION [23] & 76.5 & **89.8** & 57.5 & 78.5 & 73.8 & **87.8** & 85.9 & 81.3 \\ MR-CNN [48] & 78.2 & 87.8 & 60.3 & 85.2 & 73.7 & 87.2 & 86.5 & 85.0 \\ SSD300 [4] & 77.5 & 88.1 & 60.3 & 81.5 & **77.0** & 86.1 & 87.5 & 83.9 \\ RON [21] & 76.6 & 87.4 & 57.9 & 81.3 & 74.1 & 84.1 & 85.3 & 83.5 \\ SSD-TSEFFM300 [46] & 78.6 & 89.1 & 60.3 & **85.6** & 75.7 & 85.6 & **88.3** & 84.1 \\ SSD300-FFAM & **78.7** & 86.9 & **61.7** & 85.1 & 76.4 & 85.9 & 87.5 & **86.3** \\ \hline
**Methods** & **mAP (\%)** & **Person** & **Plant** & **Sheep** & **Sofa** & **Train** & **Tv** \\ \hline R-CNN [47] & 50.2 & 53.6 & 26.7 & 56.5 & 38.1 & 52.8 & 50.2 \\ Fast R-CNN [28] & 70.0 & 69.9 & 31.8 & 70.1 & 74.8 & 80.4 & 70.4 \\ Faster R-CNN [6] & 73.2 & 76.7 & 38.8 & 73.6 & 73.9 & 83.0 & 72.6 \\ ION [23] & 76.5 & 75.3 & 49.7 & 76.9 & 74.6 & 85.2 & **82.1** \\ MR-CNN [48] & 78.2 & 76.4 & 48.5 & 76.3 & 75.5 & 85.0 & 81.0 \\ SSD300 [4] & 77.5 & **79.4** & 52.3 & 77.9 & 79.5 & 87.6 & 76.8 \\ RON [21] & 76.6 & 77.8 & 49.2 & 76.7 & 77.3 & 86.7 & 77.2 \\ SSD-TSEFFM300 [46] & 78.6 & 79.6 & **54.6** & **82.1** & **80.2** & 87.1 & 79.0 \\ SSD300-FFAM & **78.7** & 79.3 & 53.5 & 80.1 & 79.4 & **87.8** & 77.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection results of the proposed SSD300-FFAM and state-of-the-art detectors on the Pascal VOC 2007 test set. All models were trained with 07+12 (VOC 2007 trainval + VOC 2012 trainval).