## 5 Experimental Section and Results
### Experiment on PASCAL VOC Dataset
The tolerance of layers with 64, 128, and 256 initial convolution kernels are \(-2\), \(-6\), and \(-18\), respectively. The layers conv4_1 to fc7 and conv6_2 are pruned in a geometric progression, as shown in Equation (9), since their kernel numbers are large. \[a_{n}=a_{0}q^{n} \tag{9}\]  
where \(a_{0}\) is the initial convolution kernel number; \(a_{n}\) is the convolution kernel number after the \(n\)th pruning; and \(q\) is the common ratio. The common ratio is 0.8 for the layers with 512 or 1024 convolution kernels. We use this prune criterion to prune the improved network six times. The details of each layer are shown in Table 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MAP (\%)** & **Model Size (M)** & **Time (ms)** \\ \hline Faster R-CNN [10] & 73.2 & 579 & 289.6 \\ YOLO [11] & 63.4 & 194 & 46.6 \\ SSD [12] & 74.3 & **100** & **36.2** \\ DSSD [32] & 76.3 & 632 & 162.9 \\ Our method & **77.6** & 115 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of the model size and elapsed time on the PASCAL VOC dataset. In Figure 7, after three prunings, the mAP of the model is 76.82%, which is 0.81% lower than the unpruned network. After the fourth, fifth and sixth pruning, the mAP of the model is 76.45%, 75.55% and 74.38%, respectively. Compared with the unpruned network, the mAP of the model decreases by 1.18%, 2.08% and 3.95%, respectively. We stop pruning after three times because our pruning criterion is that the precision loss should be less than 1%. Table 4 shows the mAP, model size and elapsed time with six prunings. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Kernel Numbers** & **Pruning Times** & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline conv1\_1 & 64 & 62 & 60 & 58 & 56 & 54 & 52 \\ conv1\_2 & 64 & 62 & 60 & 58 & 56 & 54 & 52 \\ conv2\_1 & 128 & 122 & 116 & 110 & 104 & 98 & 92 \\ conv2\_2 & 128 & 122 & 116 & 110 & 104 & 98 & 92 \\ conv3\_1 & 256 & 238 & 220 & 202 & 184 & 166 & 148 \\ conv3\_2 & 256 & 238 & 220 & 202 & 184 & 166 & 148 \\ conv4\_1 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ conv4\_2 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ conv4\_3 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ conv5\_1 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ conv5\_2 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ conv5\_3 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ deconv\_5\_3 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ deconv\_4\_3 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ fc6 & 1024 & 820 & 656 & 524 & 420 & 336 & 270 \\ fc7 & 1024 & 820 & 656 & 524 & 420 & 336 & 270 \\ conv6\_1 & 256 & 238 & 220 & 202 & 184 & 166 & 148 \\ conv6\_2 & 512 & 410 & 328 & 262 & 210 & 168 & 135 \\ conv7\_1 & 128 & 122 & 116 & 110 & 104 & 98 & 92 \\ conv7\_2 & 256 & 238 & 220 & 202 & 184 & 166 & 148 \\ conv8\_1 & 128 & 122 & 116 & 110 & 104 & 98 & 92 \\ conv8\_2 & 256 & 238 & 220 & 202 & 184 & 166 & 148 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SSD network structure with six recursive prunings. Figure 7: The mAP with 6 prunings. In Table 4, the mAP loss is only 0.81% after three prunings. The model size was reduced from 115 M to 40.7 M. The elapsed time for a single image was reduced from 50 ms to 35.5 ms. Compared with the original SSD network, the mAP increased by 2.52%, while the elapsed time was reduced by 0.7 ms. In order to test the scalability of the model, we tested 4952 images on the PASCAL VOC test set. The test results, with a confidence threshold of 0.3, are shown in Table 5.  
As can be seen from Table 5, among the 14,976 objects in 4952 images, 14,679 were detected correctly, 64 were mis-detected and 233 were missed. Among the 20 types of objects, the highest accuracy was 100%, the lowest accuracy was 95.0%, and the overall accuracy was 98.01%. This proves that the network has a good scalability. Figure 8 shows the detection results of the original and improved SSD on the PASCAL VOC dataset. The left column is the detection result of the original SSD network.