# Object Detection Algorithm Based on Multiheaded Attention
## 4 Experiments
### Results on Pascal VOC 2007
#### 4.2.2 Small Objects Detection  
We manually screened 541 images which contained small objects from the PASCAL VOC 2007 test set to better evaluate our model. Our MANet300 (to ensure the fairness of the experiment, the backbone network selected VGG16), improved its mAP response by 2.6% compared to the original SSD300. In addition, we visualized some of the results, as shown in Figure 5. From the figure, we can clearly see that our method is beneficial to the detection of small targets.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **Input Resolution** & **GPU** & **FPS** & **mAP (\%)** \\ \hline Two-stage & & & & & \\ Fast R-CNN [12] & VGG16 & \(1000\times 600\) & Titan X & 0.5 & 70.0 \\ Faster R-CNN [3] & VGG16 & \(1000\times 600\) & Titan X & 7.0 & 73.2 \\ OHEM [28] & VGG16 & \(1000\times 600\) & Titan X & 7.0 & 74.6 \\ HyperNet [29] & VGG16 & \(1000\times 600\) & Titan X & 0.9 & 76.3 \\ ION [30] & VGG16 & \(1000\times 600\) & Titan X & 1.3 & 76.5 \\ Faster R-CNN [3] & ResNet-101 & \(1000\times 600\) & K40 & 2.4 & 76.4 \\ R-FCN [5] & ResNet–101 & \(1000\times 600\) & Titan X & 9.0 & 80.5 \\ CoupleNet [31] & ResNet–101 & \(1000\times 600\) & Titan X & 8.2 & 82.7 \\ \hline Single-stage & & & & & \\ YOLO [1] & GoogleNet & \(448\times 448\) & Titan X & 45.0 & 63.4 \\ YOLOv2 [32] & DarkNet19 [32] & \(544\times 544\) & Titan X & 40.0 & 78.6 \\ DSOD300 [33] & DenseNet [34] & \(300\times 300\) & Titan X & 17.4 & 77.7 \\ SSD300 [2] & VGG16 & \(300\times 300\) & Titan X & 46.0 & 77.2 \\ SSD300 [2] & VGG16 & \(300\times 300\) & 1080 Ti & 71.0 & 77.7 \\ SSD512 [2] & VGG16 & \(512\times 512\) & Titan X & 19.0 & 79.8 \\ SSD321 [20] & ResNet–101 & \(321\times 321\) & Titan X & 11.2 & 77.1 \\ SSD513 [20] & ResNet–101 & \(513\times 513\) & Titan X & 6.8 & 80.6 \\ DSSD321 [20] & ResNet–101 & \(321\times 321\) & Titan X & 9.5 & 78.6 \\ DSSD513 [20] & ResNet–101 & \(513\times 513\) & Titan X & 5.5 & 81.5 \\ Feature-fused SSD [22] & VGG16 & \(300\times 300\) & Titan X & 43.0 & 78.9 \\ R-SSD300 [17] & VGG16 & \(300\times 300\) & Titan X & 35.0 & 78.5 \\ R-SSD512 [17] & VGG16 & \(512\times 512\) & Titan X & 16.6 & 80.8 \\ FSSD300 [18] & VGG16 & \(300\times 300\) & 1080 Ti & 65.8 & 78.8 \\ FSSD512 [18] & VGG16 & \(512\times 512\) & 1080 Ti & 35.7 & 80.9 \\ ESSD300 [19] & VGG16 & \(300\times 300\) & - & 52.0 & 79.2 \\ ESSD512 [19] & VGG16 & \(512\times 512\) & - & 18.6 & 82.4 \\ \hline MANet300 & VGG16 & \(300\times 300\) & 1080 Ti & 50.0 & 79.1 \\ MANet300 & ResNet-101 & \(300\times 300\) & 1080 Ti & 37.0 & 79.4 \\ MANet320 & ResNet–101 & \(320\times 320\) & 1080 Ti & 34.0 & 80.2 \\ MANet512 & ResNet–101 & \(512\times 512\) & 1080 Ti & 25.0 & 82.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of speed and accuracy based on PASCAL VOC2007 tests (We have improved on the basis of SSD, only improved the network structure, and did not introduce other data enhancement methods other than the data enhancement methods mentioned in SSD).  
Figure 4: Detection examples on the VOC 2007 test-dev with the SSD320/MANet 320 model. For each pair, the images on the left are the results of the SSD, and the images on the right are the results of MANet.  
Figure 5: Detection examples on the VOC 2007 test-dev with the SSD300/MANet 300 model. For each pair, the images on the left are the results of the SSD, and the images on the right are the results of MANet.