Abstract
This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positives. The cables also achieve quite high average precision (96.8%) and accuracy (98.4%) due to their high sampling and isolated position in the railroad corridor.
Highlights
Rail transportation is one of most popular means of traveling all over the world
Parts of this figure represents the points of class 2 and the white color in Figure 12a,b indicates the Results and Discussion height jumps that may be induced by the rail tracks or external objects
This study proposes a novel method for automatic classification of urban railroad point clouds into three classes of rail track, contact cable, and catenary cable
Summary
Rail transportation is one of most popular means of traveling all over the world. 22.7 billion passengers in Japan use the rail transportation every year. Rail freight is a very common means of goods transportation. The rail freight of Russia and the USA, for instance, constitute up to 65% and 42% of their total freight, respectively [1]. 15% of all accidents in the USA in 2014 were rail-related [2]. This indicates that the safety of the railroad environments is a crucial issue
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