Large Scale Asset Detection Within Railway Scene Point Cloud Data From Mobile Laser Scanning

To reduce greenhouse gas emissions from the transport sector, shifting to rail transport is crucial. This transition will increase the demand on existing rail infrastructure, necessitating large-scale monitoring to maintain its resilience. Point cloud data are an ideal candidate for this purpose, as they provide immediate, precise 3D geometric information independent of illumination conditions. This study investigates two object detection models, the PointPillar and the CenterPoint model, to automatically create a digital representation of the rail environment. Using a custom open dataset, these two models are evaluated to detect masts, tension rods, signals, and relay cabinets. A mean Average Precision (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mAP</i>@<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.5</i>) of 70.6% is achieved. A unique contribution of this study is an in-depth analysis of the locational error in terms of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">y</i> components of the detected positions. This analysis reveals that location accuracy is not yet sufficient for engineering applications. The analysis indicates that the largest contribution to this error originates from the random error. Additionally, this study demonstrates that transfer learning effectively reduces the labeling burden. For instance, when using 25% of the training data, the average Precision (AP) for the tension rod class improves from 9.5% without transfer learning to 70.8% with transfer learning.