# Object Detection Network Based on Feature Fusion and Attention Mechanism
## 4 Experiments
For the plant, Faster R-CNN+fusion achieved a 53.8% mAP, a 14.7 points improvement, and for the bottle, the Faster R-CNN+fusion achieved a 60.2% mAP, 10.3 points higher than the Faster R-CNN. It showed that the multi-layer feature fusion could effectively improve the detection of small targets. Our AF R-CNN outperformed the Faster R-CNN regardless of the object size. We compared our AF R-CNN with state-of-the-art methods, including Faster R-CNN [5], A-Fast-RCNN [19], CRAFT [38], and SSD [39] on the dataset, PASCAL VOC 2007. The results are shown in Table 2. Our AF R-CNN outperformed all methods on the PASCAL VOC 2007 dataset. The object detection network that we proposed in this article achieved an mAP of 75.9% on the dataset, PASCAL VOC 2007, 6 points higher than the Faster R-CNN because of feature fusion and the attention module. The above results suggest that our method enjoyed high efficiency and good performance. Figure 7 shows the visualization of the object detection. Left: The input images. Middle: The object detection of the Faster R-CNN. Right: The object detection of AF R-CNN. We found a few more \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Faster** & **Faster** & **Faster** & **Faster** & **Faster** & **Ours** & **Ours** \\  & **R-CNN** & **R-CNN** & **R-CNN+Attention** & **R-CNN+Fusion** & & & \\ \hline Training data & 07 & 07+12 & 07 & 07 & 07 & 07+12 \\ mAP & 69.9 & 73.2 & 70.3 & 75.0 & 75.9 & 79.7 \\ areo & 70.0 & 76.5 & 71.4 & 73.6 & 76.2 & 83.0 \\ bike & 80.6 & 79.0 & 81.4 & 82.4 & 81.2 & 87.0 \\ bird & 70.1 & 70.9 & 63.9 & 75.1 & 77.9 & 81.4 \\ boat & 57.3 & 65.5 & 60.5 & 62.3 & 66.2 & 74.0 \\ bottle & 49.9 & 52.1 & 47.8 & 60.2 & 62.8 & 68.5 \\ bus & 78.2 & 83.1 & 79.5 & 80.2 & 80.2 & 87.7 \\ car & 80.4 & 84.7 & 81.5 & 83.3 & 86.3 & 88.0 \\ cat & 82.0 & 86.4 & 82.1 & 83.6 & 87.5 & 88.1 \\ chair & 52.2 & 52.0 & 50.3 & 59.3 & 56.9 & 62.4 \\ cow & 75.3 & 81.9 & 75.8 & 77.2 & 85.1 & 86.8 \\ table & 67.2 & 65.7 & 67.6 & 74.5 & 71.3 & 70.8 \\ dog & 80.3 & 84.8 & 81.6 & 84.8 & 87.2 & 88.6 \\ horse & 79.8 & 84.6 & 81.8 & 86.5 & 86.2 & 87.3 \\ mbike & 75.0 & 77.5 & 76.1 & 78.4 & 80.3 & 83.8 \\ person & 76.3 & 76.7 & 77.8 & 80.9 & 79.6 & 82.8 \\ plant & 39.1 & 38.8 & 35 & 53.8 & 47.3 & 53.2 \\ sheep & 68.3 & 73.6 & 68.8 & 70.4 & 77.3 & 81.1 \\ sofa & 67.3 & 73.9 & 67.8 & 72.2 & 75.2 & 77.6 \\ train & 81.1 & 83.0 & 83.3 & 83.2 & 79.1 & 84.3 \\ tv & 67.6 & 72.6 & 69.8 & 74.6 & 74.8 & 79.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the Faster R-CNN and AF R-CNN on the dataset, PASCAL VOC 2007. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Faster R-CNN** & **A-Fast-RCNN**[28] & **CRAFT**[37] & **SSD**[38] & **Ours** \\ \hline VOC 2007 & 69.9 & 71.4 & 75.7 & 71.6 & 75.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison between the proposed method with the existing models on the dataset, PASCAL VOC 2007.  
representative pictures. First, in order to see the effect of our method on the object size, we selected a big plant (the fourth row) and a small bird (the second row). To assess the effectiveness of our approach to multiple goals and target defects, we chose the pictures in the first, third, and fifth cows. Of course, these pictures had crossovers, such as the last picture had many objects and the objects were very small. As can be seen from the results shown in Figure 7, the effect of the overall network was improved. The attention module helped detect significant targets while the feature fusion could effectively detect small targets. The experiments proved that our proposed method was more effective than Faster R-CNN.