# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 4 Experiments
### Detection Examples  
In order to intuitively compare the performance of FFESSD and SSD, as shown in Figure 4, a certain number of images were randomly sampled from the PASCAL VOC2007 test set and the outcomes were  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Backbone** & **Input Size** & **GPU** & **SPEED (FPS)** & **mAP (\%)** \\ \hline R-CNN [5] & AlexNet & \(1000\times 600\) & - & 0.07 & 50.2 \\ SPP-NET [8] & AlexNet & \(224\times 224\) & - & 0.5 & 63.1 \\ Fast R-CNN [6] & VGG16 & \(1000\times 600\) & Titan X & 0.5 & 70.0 \\ Faster R-CNN [7] & VGG16 & \(1000\times 600\) & Titan X & 7.0 & 73.2 \\ HyperNet [30] & VGG16 & \(1000\times 600\) & Titan X & 0.9 & 76.3 \\ OHEM [31] & VGG16 & \(1000\times 600\) & Titan X & 7.0 & 74.6 \\ ION [32] & VGG16 & \(1000\times 600\) & Titan X & 1.3 & 76.5 \\ \hline YOLO [9] & GoogleNet & \(448\times 448\) & Titan X & 45.0 & 63.4 \\ SSD300 [10] & VGG16 & \(300\times 300\) & 1080 Ti & 71.0 & 77.2 \\ DSSD321 [11] & ResNet-101 [17] & \(321\times 321\) & Titan X & 9.5 & 78.6 \\ FSSD300 [13] & VGG16 & \(300\times 300\) & 1080 Ti & 65.8 & 78.8 \\ R-SSD300 [15] & VGG16 & \(300\times 300\) & Titan X & 35.0 & 78.5 \\ ESSD300 [33] & VGG16 & \(300\times 300\) & - & 54 & 78.7 \\ DOSD300 [34] & DenseNet [18] & \(300\times 300\) & Titan X & 17.4 & 77.7 \\ Feature-fused SSD [35] & VGG16 & \(300\times 300\) & Titan X & 43.0 & 78.9 \\ FFESSD300 (ours) & VGG16 & \(300\times 300\) & 1080 Ti & 54.3 & 79.1 \\ \hline SSD512 [10] & VGG16 & \(512\times 512\) & Titan X & 19.0 & 79.8 \\ DSSD513 [11] & ResNet-101 [17] & \(513\times 513\) & Titan X & 5.5 & 81.5 \\ FSSD512 [13] & VGG16 & \(512\times 512\) & 1080 Ti & 35.7 & 80.9 \\ R-SSD512 [15] & VGG16 & \(512\times 512\) & Titan X & 16.6 & 80.8 \\ ESSD512 [33] & VGG16 & \(512\times 512\) & - & 20.5 & 81.7 \\ FFESSD512 (ours) & VGG16 & \(512\times 512\) & 1080 Ti & 30.2 & 81.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of speed and accuracy based on PASCAL VOC2007 tests. (For fair comparison, as introduced in Section 3, we have improved on the basis of SSD and only improved the network structure).  
compared. As shown in Figure 4, in the upper three rows of images, we can see that SSD often detects a single object with various overlapping boxes. However, the detection results of the proposed FFESSD algorithm in the same picture did not show the Box-in-Box status. FFESSD uses the Feature Fusion Module (FFM) to combine the feature maps of each layer in the feature pyramid, so as to obtain more image context information that is conducive to improving the detection accuracy. In addition, as far as we know, PASCAL VOC2007 test set contains 567 small targets (area <32 \(\times\) 32 pixel) that we selected to better evaluate our model. Our FFESSD300, improved its mAP response by 2.8% compared to the conventional SSD. We visualized some of the results, and as shown in the lower three rows of Figure 4, it can be shown that SSD misses a few small targets, and we can clearly see that our FFESSD has a better detection effect on small targets than SSD algorithm, because the proposed feature enhancement module (SFE and DFE) can improve the ability to extract semantic information of small targets.