## 4 Experimental Results and Analysis
### Comparison of Fusion Methods  
Experiments are performed on the fusion of different shallow feature layers, and the influence of the feature information extracted by the fused feature layer on the detection effect is analyzed. The test results are shown in Table 3. We set the original Conv4_3 layer's feature map as the basic feature map size to compare the fusion effects of Conv3_3, Conv5_3, and Conv6. It can be seen in Table 3, that the fusion of deeper convolutions (Conv5_3 and Conv6) can improve the detection accuracy. Conv5_3 and Conv6 have a larger receptive field, but because the Conv6 Ayer uses dilated convolution and introduces more background noise, the fusion effect with Conv5_3 is more obvious. For the choice of fusion method, concatenate fusion method can reduce the interference of background noise information on the detection results, while the element-sum fusion method can increase the perception of context information. It can be seen from Table 3 that the effect of the concatenate fusion method was better than the elements fusion method.  
We compare the visualization of the Conv4_3 layer feature mapping process between the SSD model and the improved model. As shown in Figure 8, the SSD model retains less detailed information about the target feature, and the feature extraction is not very complete. This causes the SSD algorithm to have a weak detection effect on small objects. In contrast, the features extracted by the feature layer that combines Conv4_3 and Conv5_3 can better characterize the characteristics of the object, the semantic information is richer, and the retention of detailed information is more sufficient.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **SSD** &
\begin{tabular}{c} **Faster** \\ **RCNN** \\ \end{tabular} & **DSSD** & **YOLO** & **Proposed** \\ \hline model & VGG16 & ResNet & ResNet & GoogleNet-9 & VGG16 \\ _mAP_ (\%) & 77.5 & 76.4 & 78.6 & 63.4 & **80.7** \\ aero & 79.5 & 79.8 & 81.9 & 79 & **89.4** \\ bike & 83.9 & 80.7 & 84.9 & 74.3 & **86.7** \\ bird & 76.0 & 76.2 & 80.5 & 62.5 & **81.7** \\ boat & 69.6 & 68.3 & 68.4 & 42.6 & **72.9** \\ bottle & 50.5 & 55.9 & 53.9 & 42.5 & **65.3** \\ bus & 87.0 & 85.1 & 85.6 & 68.3 & **87.2** \\ car & 85.7 & 85.3 & 86.2 & 62.1 & **88.2** \\ cat & 88.1 & 89.8 & 88.9 & 81.4 & **91.1** \\ chair & 60.3 & 56.7 & 61.1 & 42.3 & **62.5** \\ person & 79.4 & 78.4 & 79.7 & 63.5 & **87.7** \\ plant & 52.3 & 41.7 & 51.7 & 41.6 & **55.2** \\ sheep & 77.9 & 78.6 & 78.0 & 65.2 & **78.7** \\ sofa & 79.5 & 79.8 & **80.9** & 54.8 & 78.9 \\ train & 87.6 & 85.3 & 87.2 & 74.9 & **88.2** \\ tv & 76.8 & 72.0 & 79.4 & 62.5 & **79.2** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison results of accuracy under different categories.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Fusion Method** & _mAP_ (\%) \\ \hline Conv4\_3 & concatenate & 79.3 \\ Conv4\_3 + Conv3\_3 & concatenate & 79.5 \\ Conv4\_3 + Conv6 & concatenate & 80.3 \\ Conv3\_3 + Conv4\_3 + Conv5\_3 & concatenate & 80.4 \\ Conv4\_3 + Conv5\_3 & concatenate & 80.7 \\ Conv4\_3 + Conv5\_3 & elementsum & 80.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results of different fusion methods algorithms.