# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 4 Experiments
### Fuzzy Target Detection  
In addition to comparing the performance of the algorithms in the standard train and test dataset, the FFESD and SSD algorithms were used to detect the fuzzy images caused by focusing and haze  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Method** & **Backbone** & **mAP** & **Aero** & **Bike** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** & **Cat** & **Chair** & **Cow** \\ \hline SSD300 & VGG16 & 77.2 & 78.8 & 85.3 & 75.7 & 71.5 & 49.1 & 85.7 & 86.4 & 87.8 & 60.6 & 82.7 \\ SSD + FFM300 & VGG16 & 78.8 & 83.2 & 85.8 & 78.2 & 73.8 & 50.4 & 87.6 & 88.1 & 88.9 & 63.5 & 84.4 \\ SSD512 & VGG16 & 79.5 & 84.8 & 85.1 & 81.5 & 73.0 & 57.8 & 87.8 & 88.3 & 87.4 & 63.5 & 85.4 \\ SSD + FFM512 & VGG16 & 81.2 & 85.3 & 87.6 & 82.6 & 74.6 & 59.0 & 88.9 & 88.8 & 89.3 & 64.8 & 85.6 \\ \hline
**Method** & **Backbone** & **mAP** & **Table** & **Dog** & **Horse** & **Mbike** & **Person** & **Plant** & **Sheep** & **Sofa** & **Train** & **Tv** \\ \hline SSD300 & VGG16 & 77.2 & 76.5 & 84.9 & 86.7 & 84.0 & 79.2 & 51.3 & 77.5 & 78.7 & 86.7 & 76.2 \\ SSD + FFM300 & VGG16 & 78.8 & 78.8 & 86.1 & 88.3 & 85.2 & 80.1 & 52.1 & 77.3 & 78.9 & 87.5 & 77.3 \\ SSD512 & VGG16 & 79.5 & 73.2 & 86.2 & 86.7 & 83.9 & 82.5 & 55.6 & 81.7 & 79.0 & 86.6 & 81.0 \\ SSD + FFM512 & VGG16 & 81.2 & 77.2 & 87.5 & 88.9 & 87.5 & 83.7 & 57.1 & 83.9 & 82.2 & 87.7 & 81.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of SSD and SSD + FFM.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Method** & **Backbone** & **mAP** & **Aero** & **Bike** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** & **Cat** & **Chair** & **Cow** \\ \hline SSD300 & VGG16 & 77.2 & 78.8 & 85.3 & 75.7 & 71.5 & 49.1 & 85.7 & 86.4 & 87.8 & 60.6 & 82.7 \\ SSD + SFE300 & VGG16 & 78.7 & 84.1 & 86.4 & 77.6 & 73.0 & 50.6 & 87.2 & 88.3 & 88.4 & 62.9 & 84.0 \\ SSD + DFE300 & VGG16 & 78.5 & 80.2 & 85.9 & 75.9 & 72.8 & 48.5 & 87.1 & 88.4 & 88.1 & 62.5 & 83.7 \\ SSD512 & VGG16 & 79.5 & 84.8 & 85.1 & 81.5 & 73.0 & 57.8 & 87.8 & 88.3 & 87.4 & 63.5 & 85.4 \\ SSD + SFE512 & VGG16 & 81.4 & 86.5 & 88.0 & 82.6 & 74.2 & 59.1 & 88.3 & 88.9 & 89.4 & 66.1 & 85.7 \\ SSD + DFE512 & VGG16 & 81.2 & 86.1 & 87.4 & 81.7 & 73.8 & 57.6 & 88.1 & 89.1 & 89.3 & 65.7 & 85.3 \\ \hline
**Method** & **Backbone** & **mAP** & **Table** & **Dog** & **Horse** & **Mbike** & **Person** & **Plant** & **Sheep** & **Sofa** & **Train** & **Tv** \\ \hline SSD300 & VGG16 & 77.2 & 76.5 & 84.9 & 86.7 & 84.0 & 79.2 & 51.3 & 77.5 & 78.7 & 86.7 & 76.2 \\ SSD + SFE300 & VGG16 & 78.7 & 78.6 & 85.3 & 88.6 & 85.1 & 79.8 & 52.6 & 77.6 & 78.6 & 87.1 & 77.5 \\ SSD + DFE300 & VGG16 & 78.5 & 77.5 & 85.1 & 89.2 & 84.7 & 79.4 & 54.6 & 77.4 & 80.3 & 87.8 & 78.3  
problems. The detection results are shown in Figure 5. From the figure, we can clearly see that our proposed FFESSD is beneficial to the detection of fuzzy targets. This is because the Feature Fusion Module is added in FFESSD, which can more comprehensively understand the feature information from context information so that it can better distinguish between the background and the fuzzy targets, and determine the location of fuzzy targets through the contextual information.