RailSeg: Learning Local–Global Feature Aggregation With Contextual Information for Railway Point Cloud Semantic Segmentation

Jiang, Tengping; Yang, Bisheng; Wang, Yongjun; Dai, Lei; Qiu, Bo; Liu, Shan; Li, Shiwei; Zhang, Qinyu; Jin, Xin; Zeng, Wenjun

doi:10.1109/tgrs.2023.3319950

Cited by 4 publications

(2 citation statements)

References 112 publications

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“…Due to the limited railway data, the trained network is prone to overfitting, making it difficult to ensure the algorithm's robustness in other scenes. Jiang et al [28] proposed a framework named RailSeg, which contains integrated local-global feature extraction, spatial context aggregation, and semantic regularization. This method is computationally intensive and is difficult to be applied in real time.…”

Section: Lidar-based Approachmentioning

confidence: 99%

“…So far, there is limited research on the real-time segmentation of distant railway tracks using single-frame LiDAR data [14]. While some studies [25,[28][29][30] claim high precision and recall in railway track point cloud segmentation, most of them are conducted on non-real-time processing using data collected from Mobile Laser Scanning (MLS). Moreover, these methods rely on the elevation of the point cloud for segmentation, which may fluctuate significantly due to the installation angle of the LiDAR sensor, terrain variations, and train vibrations, resulting in misclassification of track beds and rail tracks.…”

Section: Lidar-based Approachmentioning

confidence: 99%

See 1 more Smart Citation

A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

Tang,

Cao,

Yang

et al. 2024

Remote Sensing

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Railway track detection, which is crucial for train operational safety, faces numerous challenges such as the curved track, obstacle occlusion, and vibrations during the train’s operation. Most existing methods for railway track detection use a camera or LiDAR. However, the vision-based approach lacks essential 3D environmental information about the train, while the LiDAR-based approach tends to detect tracks of insufficient length due to the inherent limitations of LiDAR. In this study, we propose a real-time method for railway track detection and 3D fitting based on camera and LiDAR fusion sensing. Semantic segmentation of the railway track in the image is performed, followed by inverse projection to obtain 3D information of the distant railway track. Then, 3D fitting is applied to the inverse projection of the railway track for track vectorization and LiDAR railway track point segmentation. The extrinsic parameters necessary for inverse projection are continuously optimized to ensure robustness against variations in extrinsic parameters during the train’s operation. Experimental results show that the proposed method achieves desirable accuracy for railway track detection and 3D fitting with acceptable computational efficiency, and outperforms existing approaches based on LiDAR, camera, and camera–LiDAR fusion. To the best of our knowledge, our approach represents the first successful attempt to fuse camera and LiDAR data for real-time railway track detection and 3D fitting.

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Section: Lidar-based Approachmentioning

confidence: 99%

Section: Lidar-based Approachmentioning

confidence: 99%

A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

Tang,

Cao,

Yang

et al. 2024

Remote Sensing

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A fast railway track surface extraction method based on bidirectional cloth simulated point clouds

Shi,

Yang,

Kou

et al. 2024

Optics and Lasers in Engineering

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RailPC: A large‐scale railway point cloud semantic segmentation dataset

Jiang,

Li,

Zhang

et al. 2024

CAAI Trans on Intel Tech

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Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value, but its development is severely hindered by the lack of suitable and specific datasets. Additionally, the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non‐overlapping special/rare categories, for example, rail track, track bed etc. To harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation, we introduce RailPC, a new point cloud benchmark. RailPC provides a large‐scale dataset with rich annotations for semantic segmentation in the railway environment. Notably, RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning (MLS) point cloud dataset and is the first railway‐specific 3D dataset for semantic segmentation. It covers a total of nearly 25 km railway in two different scenes (urban and mountain), with 3 billion points that are finely labelled as 16 most typical classes with respect to railway, and the data acquisition process is completed in China by MLS systems. Through extensive experimentation, we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation results. Based on our findings, we establish some critical challenges towards railway‐scale point cloud semantic segmentation. The dataset is available at https://github.com/NNU‐GISA/GISA‐RailPC, and we will continuously update it based on community feedback.

show abstract

RailSeg: Learning Local–Global Feature Aggregation With Contextual Information for Railway Point Cloud Semantic Segmentation

Cited by 4 publications

References 112 publications

A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

A Real-Time Method for Railway Track Detection and 3D Fitting Based on Camera and LiDAR Fusion Sensing

A fast railway track surface extraction method based on bidirectional cloth simulated point clouds

RailPC: A large‐scale railway point cloud semantic segmentation dataset

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