Andert, Franz (German Aerospace Center (DLR)), Kornfeld, Nils (German Aerospace Center (DLR)), Nikodem, Florian (Deutsches Zentrum für Luft- und Raumfahrt), Li, Haiyan (Siemens Mobility GmbH), Kluckner, Stefan (Siemens Mobility GmbH), Gruber, Laura (Siemens Mobility GmbH), Kaiser, Christian (Copting GmbH)

Automatic Condition Monitoring of Railway Overhead Lines from Close-Range Aerial Images and Video Data

Scheduled for presentation during the Regular Session "UAS Applications IV" (FrA3), Friday, September 4, 2020, 09:00−09:20, Edessa

2020 International Conference on Unmanned Aircraft Systems (ICUAS), September 1-4, 2020 (Postponed from June 9-12, 2020), Athens, Greece

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

This paper is about automated condition monitoring of critical railway infrastructure using unmanned aircraft systems as flying sensors. As far as possible, automation shall include flight guidance and management as well as automated processing of large sensor data sets. Since a commercial solution must consider the regulatory framework on remotely piloted aircraft systems, the paper discusses legal issues to make allowance for flights beyond visual line of sight. The work described here is focused on Europe and Germany, however, the major principles are likely to be adaptable to other countries. Next to that, the paper presents a strategy for automated image and video data processing. It consists of a super-resolution approach where onboard video camera data from typical off-the-shelf drones can replace higher-resolution still imagery and thus avoid the necessity to use special flight systems, and a deep-learning approach where specific elements are to be detected in the images. With data from flight tests over railway overhead lines, the paper shows an automated detection of rod insulators. Moreover, it presents resolution improvements from video data so that off-the-shelf camera drones can be qualified for the detection of small defects.