## 5 Experimental Section and Results
### Experiment on High-Speed Railway Dataset  
For the high-speed railway, any objects intruding the railway clearance will do great threat to normal operation. According to statistics, almost all intruding objects are pedestrians and normal running trains. For intrusion detection, we should detect all the intruding pedestrians and trains. In order to test the effect of the model on the detection of high-speed railway objects intrusion, our improved SSD model obtained from the PASCAL VOC dataset was directly used for the railway intruding detection. We selected 1000 samples containing pedestrians and trains from the railway dataset. The test results are shown in Table 6.  
In Table 6, the mAP of pedestrians and trains are 41.25% and 49.36%, respectively. The model trained with the PASCAL VOC dataset is not good enough for high speed railway intruding detection. So we need to use a high-speed railway dataset that includes pedestrians and trains. We collected samples from the HangYong and BaoLan high-speed railway line at different time periods, different weather conditions, and different speed conditions. Then, we converted a series of railway traffic videos into a sequential frame of images. Because of the similar content between adjacent sequences, we sampled the image every five frames. In total, we labeled 14,760 image frames and obtained 21,053 pedestrian samples and 9231 train samples, shown in Table 7. Some of the data samples are shown in Figure 9. \begin{table}
\begin{tabular}{c c c} \hline
**mAP (\%)** & **Pedestrians** & **Trains** \\ \hline
45.3 & 41.25 & 49.36 \\ \hline \end{tabular}
\end{table} 
Table 6: Experiment results on the railway dataset. Figure 8: Detection results comparison between the original SSD and our improved SSD. After the dataset setup, the improved SSD network was trained with our high-speed railway dataset. We took 80% of these samples for training and validation, and the rest for testing. The mAP, model size and elapsed time results of our method without pruning are compared with those of Faster R-CNN, YOLO, SSD and DSSD in Table 8.  
In Table 8, compared to the second best method, our method improved the precision of pedestrian detection by 2.91%. The improvement on the train detection precision is not very obvious because the train samples are all large objects. In general, our method gets the best performance in precision, the second best in model size and the third best in elapsed time. In the above experiment, we used RGB images to train the network. In order to compare the effects of single-channel and three-channel images on the final accuracy of the network, we transformed the RGB images into brightness images for training. The training results are shown in Table 9.  
From Table 9, we can see that the recognition performance of the network degrades due to the lack of chrominance information in the brightness images. But the improved SSD network still has a higher performance than other state-of-the-art networks. Compared with the RGB images, the performance of the brightness images is worse. Consequently, we use the RGB images for the recognition. In order to reduce the model size and elapsed time, the improved SSD is pruned with the same L1 norm criterion. The pruning results are shown in Table 10. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **mAP (\%)** & **Pedestrian** & **Train** & **Model Size (M)** & **Time (ms)** \\ \hline Faster-Rcnn & 75.38 & 70.68 & 80.05 & 512 & 190.2 \\ YOLO & 72.31 & 64.98 & 79.64 & 192 & 38.2 \\ SSD & 75.12 & 69.86 & 80.38 & **90** & **33.5** \\ DSSD & 79.12 & 77.25 & 80.90 & 569 & 150.8 \\ Our Method & **80.54** & **80.16** & **80.92** & 105 & 48.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results comparison of different methods on the railway dataset.