## 5 Experiments
### Comparison with Existing Methods
Furthermore, our method achieves an inference time of 0.270 s per frame, which is superior to the method proposed by [PERSON] et al. The experimental results presented in Table 3 further illustrate the performance metrics of different methods at various working distances. At a distance of 40 m, we observed that methods solely based on LiDAR achieved passable accuracy, with a rate of 87.2%. However, due to the lack of crucial input data, LiDAR-based methods are unable to effectively predict longer distances. Therefore, both [PERSON] et al. 's method and our method utilize cameras effectively to extend the effective working distance. [PERSON]'s method, employing plane fitting, exhibits increasing errors with distance, resulting in a recall rate of 98.7% at 40 m but dropping to 89.2% at around 200 m. In contrast, our method shows no significant decrease, maintaining a recall rate of 98.2% even at 200 m.  
Compared to other fusion methods, our proposed method exhibits better overall performance and does not suffer from significant accuracy degradation at different distances. As shown in Figures 15 and 16, we present two typical data cases to illustrate the superiority of our approach. As shown in Figure 16, [PERSON] et al. 's method fails to obtain correct track area point clouds, while the radar-based method cannot acquire sufficient valid points. Our method, however, analyzes and reconstructs the track in space based on limited points, resulting in the correct track area. In Figure 17, the intruding object is located far from the device and there are some errors in the semantic segmentation results, incorrectly identifying the steel bars on the left as the track and considering part of it as the track area. Solely relying on LiDAR-based methods, due to insufficient valid points, cannot identify the position of the track area at a distance and therefore cannot determine the intrusion situation in the distance. As for [PERSON]'s method, since there is no post-processing of the semantic segmentation results, errors in the semantic segmentation results often directly affect the final result of the algorithm. Meanwhile, our method effectively reconstructs the correct track area and accurately identifies the intrusion object. Table 4 illustrates the comparison of our algorithm's results with other methods in different scenarios in the dataset we proposed. The conclusions drawn from Table 4 further support our argument. Our method demonstrates superior performance at railway switches, with a recall rate of 100% and an accuracy rate of 93.8%, outperforming other methods. This is attributed to the challenges faced by radar-based methods in calculating complex railroads and the inclination of fitted planes due to semantic segmentation errors in [PERSON] et al. 's method. Our approach, based on track reconstruction, maximally adapts to complex scenarios. However, the performance on curved tracks is slightly inferior to methods based on LiDAR. Our method achieves a recall rate of 63.5%, while LiDAR-based methods achieve 93.4%. This is because the data collection time for this scenario is mostly during the night, when cameras cannot obtain effective semantic information, whereas lidar can scan the surrounding scene clearly. Nevertheless, overall, our method still demonstrates significant superiority. Figure 16: Data Case 1. Due to dim lighting conditions, the semantic segmentation algorithm fails to recognize the track area plane in the distance. Our method performs well in switchback, tunnels, and straight sections. In the straight section scenario, intrusion objects are relatively far away, making LiDAR-only methods ineffective. In the curve scenario, LiDAR-only methods benefit from their accurate geometric information, resulting in good performance in the precision metric.