## 5 Experimental Section and Results
### Experiment on PASCAL VOC Dataset  
The PASCAL VOC dataset contains 20 categories: person, aero, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, potted plant, sheep, sofa, train and TV monitor. The PASCAL VOC dataset consists of VOC2007 and VOC2012. We train our network on the VOC2007 and VOC2012 training sets and test it on the VOC2007 test set. In object detection, the mean average precision (mAP) is a commonly used evaluating indicator for detection precision. The mAP is calculated through recall and precision. Specifically, the recall and precision could be calculated as Equations (6) and (7):  
\[precision=TP/(TP+FP) \tag{6}\]  
\[recall=TP/(TP+FN) \tag{7}\]  
where \(TP\), \(FP\), \(TN\) and \(FN\) stand for true-positive, false-positive, true-negative and false-negative. Under different confidence thresholds, the two-dimensional curve with precision and recall as the horizontal and vertical coordinates can be plotted. The area under the curve is the average precision (AP), considering both the precision and recall. The average of all categories of APs is the mAP. In the experiment, we use mAP as an indicator to evaluate the network performance. The experimental results of the improved SSD with deconvolution are compared with Faster-RCNN, YOLO, Single Shot multibox Detector (SSD) and DSSD on the PASCAL VOC datasets in Table 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Faster [10]** & **YOLO [11]** & **SSD [12]** & **DSSD [32]** & **Our Method** \\ \hline mAP & 73.2 & 63.4 & 74.3 & 76.3 & **77.6** \\ Aero & 76.5 & 68.3 & 75.5 & **87.3** & 83.2 \\ Bike & 79 & 69.2 & 80.2 & 83.3 & **86.2** \\ Bird & 70.9 & 59.5 & 72.3 & **75.4** & 75.1 \\ Boat & 65.5 & 51.6 & 66.3 & **64.6** & **68.7** \\ Bottle & **52.1** & 35 & 47.6 & 46.8 & 49.9 \\ Bus & 83.1 & 72.6 & 83 & 82.7 & **87** \\ Car & 84.7 & 72.8 & 84.2 & 76.5 & **87.3** \\ Cat & 86.4 & 79.6 & 86.1 & **92.9** & 89.1 \\ Chair & 52 & 42.9 & 54.7 & **59.5** & 58 \\ Cow & 81.9 & 69.5 & 78.3 & 78.3 & **83.2** \\ Table & 65.7 & 58.4 & **73.9** & 64.3 & 72 \\ Dog & 84.8 & 71.8 & 84.5 & **91.5** & 88.1 \\ Horse & 84.6 & 79.2 & 85.3 & 86.6 & **89.6** \\ Mbike & 77.5 & 70.5 & 82.6 & 86.6 & **87.2** \\ Person & 76.7 & 65.6 & 76.2 & **82.1** & 80.6 \\ Plant & 38.8 & 37 & 48.6 & **53.3** & 51.4 \\ Sheep & 73.6 & 68.7 & 73.9 & **79.6** & **79.6** \\ Sofa & 73.9 & 68.2 & **76** & 75.7 & 74.9 \\ Train & 83 & 74.3 & 83 & 85.2 & **85.5** \\ Tv & 72.6 & 60.5 & 74 & 73.9 & **76.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PASCAL VOC detection results. In Table 1, compared with the other four state-of-art network models, the improved SSD that is proposed has the 11 highest APs out of 20 object categories. In general, our method has the best performance. Compared to Faster-RCNN, YOLO, SSD and DSSD, the mAP increases by 4.4%, 14.2%, 3.3% and 1.3%, respectively. On this basis, the elapsed time and model size of the four models are compared in Table 2.  
As shown in Table 2, compared with the other four networks, the improved SSD network model has a significant improvement in mAP. However, the elapsed time of each image is higher than SSD and YOLO. At the same time, the model size is significantly smaller than Faster R-CNN, YOLO and DSSD, but is a little bigger than SSD. In order to reduce the calculation load and improve the detection speed, the improved SSD is recursively pruned with the L1 norm criterion until the precision loss is larger than 1%. According to the difference of the convolution kernel numbers in different layers, the convolution kernels are pruned in arithmetic and geometric form at different layers. The layers Conv1_1 to conv3_3, conv6_1, and conv7_1 to conv8_2 are pruned in arithmetic form, as shown as Equation (8), because their convolution kernel numbers are small. \[a_{n}-a_{n-1}=d \tag{8}\]  
In Equation (8), \(a_{n}\) is the convolution kernel number after pruning; and \(a_{n-1}\) is the convolution kernel number before pruning. \(d\) is the tolerance.