## 6 Conclusions and Future Work  
This paper proposes a robust multimodal railway intrusion detection algorithm aimed at helping drivers assess the presence of foreign objects in the tracks, thus preventing potential accidents involving train collisions with intrusions. Our core idea revolves around using both LiDAR and cameras as sensors. We reconstruct the railway tracks using image information and utilize LiDAR scan results to detect intrusions. While LiDAR provides rich and accurate geometric information, it may fail to detect objects at longer distances. On the other hand, images captured by cameras offer rich semantic information, facilitating easy identification of track details, but they lack precise geometric information. Moreover, errors in image processing algorithms may arise due to variations in image quality and operational environments. To address these challenges, we propose a post-processing method that adaptively handles the results of image segmentation algorithms. We parse the expressions of the tracks in our defined space to determine where the train tracks willpass. Subsequently, we employ scanning algorithms to assess the intrusion status in the current environment.  
The algorithm consists of the following steps:  
* Define three coordinate systems: The LiDAR coordinate system, railway coordinate system, and camera image coordinate system. Feed the image into a neural network for semantic segmentation. Utilize the pose relationship between the camera coordinate system and the railway coordinate system to project the image results into a bird's-eye view representation in the railway coordinate system;
* Perform coarse filtering of railway points based on prior information. Design a key point search algorithm based on track tracing to search for key points on each track. Then, use predefined constraints to segment and fit the track, obtaining the analytical expression of the railway track;
* Within the delineated track area, conduct a sliding window search and set thresholds for height and point cloud density to determine the presence of intrusions within the track.  
Additionally, we collected and established a large-scale dataset known as the \"Railway Intrusion Dataset\". This dataset includes data from two types of locomotives and data from 11 railway lines, comprising a total of 7547 real-world images. Furthermore, we manually annotated four representative scenes from this dataset for quantitative analysis.  
Comparative results with other similar methods indicate that our proposed method has the longest effective working distance and highest accuracy. Furthermore, it exhibits a certain degree of robustness in extreme conditions such as adverse weather. Ablation experiments have demonstrated the effectiveness and necessity of each module in our method. In future research, we will focus on algorithm light weighting and investigate how to leverage parallel computing methods for GPU porting of the algorithm.  
Conceptualization, S.D. and Y.L.; methodology, J.C.; software, J.C.; formal analysis, J.C.; investigation, J.C.; resources, J.C.; writing--original draft preparation, J.C.; writing--review and editing, Y.L.; visualization, J.C.; supervision, S.D.; project administration, S.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data covered in this paper are available from the corresponding author upon request.  
We would like to express our gratitude to [PERSON], [PERSON] and [PERSON] from Nanjing University for their assistance in data collection for the dataset. We also extend our thanks to the technical team at Gosunsun Company for their engineering optimization of the data collection equipment.  
The authors declare no conflicts of interest.