# The Assisted Positioning Technology for High Speed Train Based on Deep Learning
## 5 Analysis of the Experimental Results
### The Influence of Model Structure and Anchor Boxes  
In this paper, the improvement in the SSD algorithm is mainly reflected in two aspects. Firstly, it inherited the optimization method in [31]. Secondly, through the analysis of the application scenario, the network was simplified, only its backbone network was extracted for modeling, and the number of anchor boxes was reduced from 4 to 2. In order to prove that this is effective, the effects of using different model structures and anchor boxes were compared and analyzed. Here, the data set is the same, containing 500 images, with the resolution of each image being 960 \(\times\) 480 pixels. Firstly, the detection accuracy and time corresponding to different model structures were compared, as shown in Table 2. Here, the number of anchor boxes is set as 4 and the time is the detection time of a kilometer post image in the verification stage after the model training was completed. From Table 2, it can be seen that the method used to extract the backbone network in [31] for modeling was effective. It not only ensured the detection accuracy, but also reduced the detection time to 13.57 ms. That is to say, the detection efficiency was improved.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Different Data Set Size** & **Accuracy** \\ \hline
60 images & 75.65\% \\
500 images & 98.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The influence of the data set.  
Figure 6: The kilometer post detection model based on an improved SSD algorithm.  
In addition, to be scale-insensitive, multi-scale feature maps that have resolutions of 1/8, 1/16, 1/32, 1/64 of the original image were used for object detection. We associated two anchor boxes with each level. In this paper, we used k-means to generate eight anchor boxes, and then assigned the clustered anchor boxes to each level of the feature maps based on the areas. At the beginning of clustering, the width and height of the largest and smallest samples were divided into eight parts, and they were set as the center of each cluster. Then, after many iterations, the cluster center coordinates and sample distribution were obtained, as shown in Figure 7. Different colors and shapes represent different clusters, and cluster centers are represented by +. Furthermore, according to the area size of all cluster centers, the width and height of anchor boxes corresponding to different resolution layers can be obtained, as shown in Table 3.  
Furthermore, Table 4 shows the detection accuracy and the time corresponding to different anchor boxes. From this, we can see that it also simultaneously ensures the accuracy and improves the detection efficiency. This proves that a design with a reduced number of anchor boxes, such as that proposed in this paper, is effective.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Different Anchor Boxes** & **Accuracy** & **Time** \\ \hline
4 & 97.81\% & 36.12 ms \\
2 & 98.92\% & 35.43 ms \\ \hline \hline \end{tabular}
\end{table}
Table 4: The influence of anchor boxes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Different Model Structure** & **Accuracy** & **Time** \\ \hline ZRNet model in [31] & 97.13\% & 49.69 ms \\ Our model in this paper & 97.81\% & 36.12 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: The influence of model structure.  
Figure 7: Cluster.