## 5 Experiments
### Discussion  
In this chapter, we conducted experiments using our self-collected and annotated dataset. As shown in Table 2, our method significantly outperforms the ones based solely on LiDAR and even [PERSON] et al.'s method in terms of quantitative results. We can explain this phenomenon based on the results in Table 3. Firstly, LiDAR-based methods lack point cloud data for the track, so even if the algorithm's accuracy is high, it cannot provide effective information to judge distant targets. Secondly, [PERSON] et al.'s method lacks post-processing, making it unable to handle cases where there are errors in the image segmentation results, thereby affecting the precision of the final results. The experiments in Table 4 further validate these conclusions across different scenarios. In summary, image-based track reconstruction adds significant value and post-processing algorithms under orthogonal projection play a crucial role in the final results.  
Furthermore, we conducted ablation experiments to verify the rationality of our algorithm modules. The experiments in Table 5 demonstrate the accuracy of our track reconstruction method based on image pixels, while those in Table 6 help us identify reasonable hyperparameters. However, based on the experimental results, we believe that there is still room for improvement in our current method, especially in high-speed motion and highly complex switchyard scenarios.  
Our experimental results far surpass methods based solely on LiDAR, primarily because we incorporate image modality as an input. While LiDAR-only methods rely on ground truth for input, our approach demonstrates significant superiority. Furthermore, our method outperforms the fusion method by [PERSON] et al. (as far as we know, the only state-of-the-art fusion method) because we independently designed a post-processing module. Therefore, even with minor errors in the semantic segmentation algorithm, our final results are notably superior to [PERSON] et al.'s approach. Furthermore, our proposed fusion method addresses three key issues. Firstly, compared to methods relying solely on vision, our approach excels because it does not require additional training or fine-tuning for specific lighting conditions, intrusion targets, or scenes. Moreover, our method can accurately determine the distance to intrusions, which is not achievable with monocular systems. Secondly, compared to methods relying solely on LiDAR, our approach remains effective even when LiDAR devices lack crucial information. Sometimes, LiDAR fails to scan track point clouds effectively, making it impossible to provide a sufficient effective distance even when using ground truth as a track area. In such cases, our method leverages rich semantic information in images to extract, project, and reconstruct tracks in 3D space, effectively mitigating overreliance on LiDAR reliability. Lastly, to the best of our knowledge, unlike the latest fusion-based state-of-the-art methods, we have additionally designed a robustness correction mechanism to enhance fault tolerance against semantic segmentation algorithm results. These three reasons make our algorithm notably superior to other state-of-the-art works.