## 5 Experiments
### Comparison with Existing Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Distance** & **Method** & **Rec.** & **F1** & **Pre.** & **Acc.** \\ \hline \multirow{4}{*}{\(<\)40 m} & LiDAR only [43; 45; 46] & 0.872 & 0.311 & 0.437 & 0.972 \\  & [PERSON] et al. [7] & 0.987 & 0.576 & 0.565 & 0.939 \\  & Ours & **0.994** & **0.986** & **0.991** & **1.000** \\ \cline{1-1}  & LiDAR only [43; 45; 46] & \textbackslash{} & \textbackslash{} & \textbackslash{} \\ \cline{1-1}  & [PERSON] et al. [7] & 0.982 & 0.526 & 0.487 & 0.949 \\ \cline{1-1}  & Ours & **0.989** & **0.972** & **0.974** & **1.000** \\ \cline{1-1}  & LiDAR only [43; 45; 46] & \textbackslash{} & \textbackslash{} & \textbackslash{} \\ \cline{1-1}  & [PERSON] et al. [7] & 0.923 & 0.397 & 0.363 & 0.944 \\ \cline{1-1}  & Ours & **0.985** & **0.910** & **0.900** & **0.997** \\ \cline{1-1}  & LiDAR only [43; 45; 46] & \textbackslash{} & \textbackslash{} & \textbackslash{} \\ \cline{1-1}  & LiDAR only [43; 45; 46] & \textbackslash{} & \textbackslash{} & \textbackslash{} \\ \cline{1-1}  & Ours & **0.982** & **0.909** & **0.900** & **1.000** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the rail-obstacle approach at different distances. Table 2 presents the comparative results of overall metrics for the methods mentioned in the paper. From the results in Table 2, we can compare and conclude that the performance of methods based solely on LiDAR is mediocre. According to this method, the recall rate is 40.1%, the precision rate is 45.2%, and the F1 score is only 35.4%. This is because such methods heavily rely on the quality of equipment and input data. Even though these methods can effectively handle existing point clouds, they struggle to perform well when the point cloud data itself lacks meaningful information. On the other hand, [PERSON] et al. 's fusion method showed a significant improvement in recall, reaching 89.2%. However, there is no significant improvement in precision and F1 score, which are only 41.8% and 37.4%, respectively. This is because their method is based on spatial plane fitting of trajectory regions obtained from semantic segmentation. However, the results of semantic segmentation are not always reliable and even slight deviations can lead to discrepancies in the fitted plane angles. Although they extended the effective working distance of the algorithm, the deviation in plane fitting led to missed detections, resulting in no significant improvement in precision and F1 score. Our method, on the other hand, focuses on improving the effective working distance and robustness of sub-modules. As a result, our false detection rate and missed detection rate are relatively low. In terms of performance metrics, our method exhibits excellent accuracy, recall, and F1 score, reaching 98.2%, 90.9%, and 90.0%, respectively. This is nearly a 200% improvement compared to methods solely based on LiDAR. Since most intrusion detection algorithms are sensitive to missed detections, for safety reasons, the penalty for missed detections far outweighs that for false alarms. Therefore, all algorithms achieve an objective level of accuracy within their effective working distances. It is worth noting that our algorithm has been optimized to achieve a processing time of 0.27 s/frame. Although this is not as fast as the 0.147 s/frame of methods based solely on LiDAR, our algorithm exhibits significantly higher accuracy than such methods. In contrast, [PERSON] et al. 's method requires approximately 3.162 s to complete processing, with the majority of the time consumed by point cloud projection and plane fitting operations. In summary, it can be observed that our proposed method demonstrates superiority across all evaluation metrics. This is because our method combines LiDAR and a camera for track reconstruction, resulting in a much greater effective working distance compared to other methods. Moreover, based on our post-processing approach, the algorithm's results are not overly reliant on the performance of devices such as LiDAR and cameras.