## 4 Dataset  
In this paper, there are two datasets. One is the open-source image dataset RailSem19 [36], which is used for training the semantic segmentation neural network. The other dataset is the tracked area intrusion detection dataset that we collected using a synchronized Alvium G5-1240 camera with a focal length of 25 mm and a LiDAR to capture image-point cloud pairs. The original resolution of this camera is 3036 \(\times\) 4024 pixels. However, for training and inference using deep learning networks, we resized the images to 512 \(\times\) 1024 pixels. Our camera is a color camera and for nighttime driving, illumination from the train's headlight was relied upon. There are two models of LiDAR: Titan-M1-R LiDAR from Neuvition and Livox-Tele15. The repetition rate of the solid-state LiDAR is chosen as 2 KHz as a frame and the resolution of the image is 3036 \(\times\) 4024, as shown in Figure 11.  
Figure 11a depicts the data acquisition device we designed, which includes a pair of parallel LiDARs and cameras. Their optical centers are aligned parallelly. Figure 11b depicts the physical setup of our data collection, while Figure 11c shows two different train heads. We used these two trains to collect data on different routes. Furthermore, as illustrated in Figure 12, to address the errors caused by vehicle motion during data collection, we employed a uniform acceleration model for motion calibration of the point cloud. Assuming the initial pose of the LiDAR scan is \(p_{i}^{1}\), we assumed that  
Figure 11: Schematic diagram of the data collection equipment and its installation positions. during the scanning process, the vehicle can be considered to undergo uniform acceleration (or uniform motion). Assuming that the train moves without any rigid body rotation during operation, only undergoing displacement transformation, we can calculate the displacement of each point relative \(p_{i}^{t}\) to \(p_{i}^{1}\) at each moment through velocity integration. Then, by applying a translation transformation, we can eliminate the motion errors. This is shown in Equations (11) and (12). \[\Delta d=\int\limits_{0}^{t}{(v_{0}+at)dt}=v_{0}t+\frac{1}{2}at^{2} \tag{11}\] \[p_{i}^{1}=p_{i}^{t}-\Delta d \tag{12}\]  
We manually calibrated the parameters required for the experiment and collected a large-scale dataset of simulated train intrusion on different subway and train routes. Where \(v_{0}\) is the current initial velocity, \(a\) is the acceleration and \(t\) is the time interval of the point cloud relative to the initial scan point. We collected a total of 11 routes, encompassing various scenes, weather conditions, and lighting conditions. There are a total of 7547 pairs of synchronized image-point cloud data. Partial images from the dataset are shown in Figure 13. For quantitative analysis, we selected four representative routes for manual annotation. We annotated the track areas in the images and whether there were intrusions in the point clouds. These four datasets include scenarios such as switchbacks, tunnels, curves, and straight sections. Additionally, they include common intrusions in railway tracks such as pedestrians of various sizes, backpacks, suitcases, and umbrellas. To validate the accuracy of railway track reconstruction, we deliberately included some objects outside the track but close to it, referred to as \"negative hard samples\". Table 1 represents the content of the dataset. The dataset was collected for four scenarios: tunnel, switchback, curves, and straightaways. The dataset comprises 156 instances of pedestrian intrusion scenes, 144 instances of umbrella intrusion scenes, 144 instances of luggage (medium-sized objects) intrusion scenes, and 60 instances of parcel (small-sized objects) intrusion scenes. Additionally, there are 253 instances of negative hard sample scenes, where objects are close to the track but do not intrude.