## 5 Experimental Section and Results
### Experiment on High-Speed Railway Dataset
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **mAP (\%)** & **Pedestrian** & **Train** & **Model Size (M)** & **Time (ms)** \\ \hline Faster-Rcnn & 73.20 & 66.72 & 79.68 & 512 & 190.0 \\ YOLO & 69.5 & 60.96 & 78.04 & 192 & 38.0 \\ SSD & 73.16 & 66.75 & 79.57 & **90** & **33.4** \\ DSSD & 76.85 & 73.43 & 80.27 & 569 & 150.7 \\ Our Method & **78.21** & **76.01** & **80.41** & 105 & 48.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results comparison of different methods on the brightness images. Figure 9: Samples of the railway dataset. In Table 10, the mAP loss is 0.66%, 1.26% and 2.13% after 4, 5 and 6 prunings. It should be noted that after 4, 5 and 6 prunings, the average precision of pedestrians decreased by 0.81%, 1.66% and 3.11%, respectively, and the average precision of trains decreased by 0.5%, 0.87% and 1.14%, respectively. The main reason for this is that the pedestrian objects are smaller than those of trains. With the same criterion as the PASCAL VOC dataset, the precision loss threshold is 1%. Consequently, we pruned the improved SSD network 4 times. After the 4 th pruning, the model size was reduced from 105 M to 24.4 M. The elapsed time for a single image was reduced from 48.3 ms to 31.0 ms. Compared with the original SSD network, the mAP increased by 4.74%, while the elapsed time was reduced by 2.5 ms. Figure 10 shows the pedestrian detection results of the original SSD (Figure 10a) and our improved SSD (Figure 10b) in the high-speed railway dataset. In the first row of Figure 10, the original SSD detects the left two pedestrians as one. However, our method detects all three pedestrians. In the second row, our method detects one more person than the original SSD does. At the same time, the confidence of all objects is improved significantly in our method. Finally, we tested with 2952 images from the test set. There are 1386 bullet trains and 3484 pedestrians (there may be multiple pedestrians in one image). We set the confidence threshold to 0.4 to test the images in the test set. The test results are shown in Table 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pruning Times** & **mAP (\%)** & **Pedestrian** & **Train** & **Model Size (M)** & **Time (ms)** \\ \hline
0 & 80.54 & 80.16 & 80.92 & 105 & 48.3 \\
1 & 80.20 & 79.79 & 80.62 & 71.8 & 41.4 \\
2 & 80.17 & 79.72 & 80.63 & 49.5 & 36.8 \\
3 & 80.09 & 79.59 & 80.59 & 34.4 & 33.2 \\
**4** & **79.88** & **79.35** & **80.42** & **24.4** & **31.0** \\
5 & 79.28 & 78.5 & 80.05 & 17.4 & 29.8 \\
6 & 78.41 & 77.05 & 79.78 & 12.6 & 27.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Pruning results of the railway dataset. Figure 10: Comparison between the original SSD and our improved SSD. (**a**) Recognition effect of the SSD. (**b**) Recognition effect of the improved SSD. As can be seen from Table 10, among the 3484 pedestrians, 3642 can be detected. 22 are missed, and the number of false detections is 0. Among the 1386 trains, all of them can be detected, and both mistakes and missed detections are 0. The detection accuracy can reach 99.55%, the mistake rate is 0, and the miss rate is 0.45%, which can effectively perform the railway clearance intrusion detection. In order to compare the influence of light intensity on the detection results, we divided the test set into two parts of 1723 strong light images and 1229 non-strong light images. Some of the data samples are shown in Figure 11a,b. The detection results are shown in Table 12.  
In Table 12, there are more missed detections for strong light images than for non-strong light images because of the strong light interference with the same confidence threshold as Table 11.