Abstract
Abstract. Transport infrastructure monitoring has lately attracted increasing attention due to the rise in extreme natural hazards posed by climate change. Mobile Mapping Systems gather information regarding the state of the assets, which allows for more efficient decision-making. These systems provide information in the form of three-dimensional point clouds. Point cloud analysis through deep learning has emerged as a focal research area due to its wide application in areas such as autonomous driving. This paper aims to apply the pioneering PointNet, and the current state-of-the-art KPConv architectures to perform scene segmentation of railway tunnels, in order to validate their employability over heuristic classification methods. The approach is to perform a multi-class classification that classifies the most relevant components of tunnels: ground, lining, wiring and rails. Both architectures are trained from scratch with heuristically classified point clouds of two different railway tunnels. Results show that, while both architectures are suitable for the proposed classification task, KPConv outperforms PointNet with F1-scores over 97% for ground, lining and wiring classes, and over 90% for rails. In addition, KPConv is tested using transfer learning, which gives F1-scores slightly lower than for the model training from scratch but shows better generalization capabilities.
Highlights
The modern society is increasingly dependent on transportation networks in its daily activities
As the application of these deep learning architectures is very limited in railway environments, this paper proposes the application of two different architectures for the classification of railway tunnels: PointNet and Kernel Point Convolution (KPConv)
This work presents the application of two deep learning models, PointNet and KPConv, for the semantic classification of 3D point clouds from railway tunnels
Summary
The modern society is increasingly dependent on transportation networks in its daily activities. Mobile Mapping Systems (MMS) have evolved rapidly during the last few years, as they have proven to be a valid technology for infrastructure monitoring applications They are mobile platforms equipped with different monitoring sensors, such as laser scanners, which are able to collect 3-dimensional (3D) representations of the environment in the form of point clouds on an accurate and efficient manner. MMS typically include other remote sensing components such as positioning sensors or RGB cameras, which provide additional data that is coupled to the point cloud. Such a point cloud is a sparse non-grid structured data and its processing is more challenging than that of 2dimensional grids.
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