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A platform for research: civil engineering, architecture and urbanism
Point cloud semantic segmentation of complex railway environments using deep learning
Abstract Safety of transportation networks is of utmost importance for our society. With the emergency of digitalization, the railway sector is accelerating the automation in inventory and inspection procedures. Mobile mapping systems allow capturing three-dimensional point clouds of the infrastructure in short periods of time. In this paper, a deep learning methodology for semantic segmentation of railway infrastructures is presented. The methodology segments both linear and punctual elements from railway infrastructure, and it is tested in four scenarios: i) 90 km-long railway; ii) 2 km-long low-quality point clouds; iii) 400 m-long high-quality point clouds; iv) 1.4 km-long railway recoded with aerial mapping system. The longest one is used for training and testing, obtaining mean accuracy greater than 90%. The other scenarios are used only for testing, and qualitative results are discussed, proving that the method can be applied to new scenarios that significantly differ in terms of data quality and resolution.
Highlights Railway point clouds automatic semantic segmentation based on deep learning. Trained and validated in 90 km long railway and tested in three new scenarios with different characteristics. Generalization to new railway environments and Lidar sensors.
Point cloud semantic segmentation of complex railway environments using deep learning
Abstract Safety of transportation networks is of utmost importance for our society. With the emergency of digitalization, the railway sector is accelerating the automation in inventory and inspection procedures. Mobile mapping systems allow capturing three-dimensional point clouds of the infrastructure in short periods of time. In this paper, a deep learning methodology for semantic segmentation of railway infrastructures is presented. The methodology segments both linear and punctual elements from railway infrastructure, and it is tested in four scenarios: i) 90 km-long railway; ii) 2 km-long low-quality point clouds; iii) 400 m-long high-quality point clouds; iv) 1.4 km-long railway recoded with aerial mapping system. The longest one is used for training and testing, obtaining mean accuracy greater than 90%. The other scenarios are used only for testing, and qualitative results are discussed, proving that the method can be applied to new scenarios that significantly differ in terms of data quality and resolution.
Highlights Railway point clouds automatic semantic segmentation based on deep learning. Trained and validated in 90 km long railway and tested in three new scenarios with different characteristics. Generalization to new railway environments and Lidar sensors.
Point cloud semantic segmentation of complex railway environments using deep learning
Grandio, Javier (author) / Riveiro, Belén (author) / Soilán, Mario (author) / Arias, Pedro (author)
2022-06-08
Article (Journal)
Electronic Resource
English
Multi-Context Point Cloud Dataset and Machine Learning for Railway Semantic Segmentation
| DOAJ | 2024