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

Andert, Franz and Kornfeld, Nils and Nikodem, Florian and Li, Haiyan and Kluckner, Stefan and Gruber, Laura and Kaiser, Christian (2020) Automatic Condition Monitoring of Railway Overhead Lines from Close-Range Aerial Images and Video Data. International Conference on Unmanned Aircraft Systems, 01.- 04. Sep. 2020, Athen, Griechenland.

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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 offthe-shelf drones can replace higher-resolution still imagery and thus avoid the necessity to use special flight systems, and a deeplearning 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.

Item URL in elib:https://elib.dlr.de/135780/
Document Type:Conference or Workshop Item (Speech)
Title:Automatic Condition Monitoring of Railway Overhead Lines from Close-Range Aerial Images and Video Data
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Andert, FranzFranz.Andert (at) dlr.dehttps://orcid.org/0000-0002-1638-7735
Kornfeld, Nilsnils.kornfeld (at) dlr.deUNSPECIFIED
Nikodem, FlorianFlorian.Nikodem (at) dlr.deUNSPECIFIED
Li, Haiyanhaiyan.li.ext (at) siemens.comUNSPECIFIED
Kluckner, Stefanstefan.kluckner (at) siemens.comUNSPECIFIED
Gruber, Lauralaura.gruber (at) siemens.comUNSPECIFIED
Kaiser, Christianckaiser (at) copting.deUNSPECIFIED
Date:4 September 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1270-1277
Status:Published
Keywords:Predictive Maintenance, Unmanned Aircraft, UAV, UAS, Railway Infrastructure, Deep Learning, Artificial Intelligence, Image Super-Resolution
Event Title:International Conference on Unmanned Aircraft Systems
Event Location:Athen, Griechenland
Event Type:international Conference
Event Dates:01.- 04. Sep. 2020
Organizer:ICUAS Association
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Rail Transport
DLR - Research area:Transport
DLR - Program:V SC Schienenverkehr
DLR - Research theme (Project):V - Digitalisierung und Automatisierung des Bahnsystems, L - The Smart Rotorcraft
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Transportation Systems > Data Management and Knowledge Discovery
Institute of Flight Systems > Safety Critical Systems&Systems Engineering
Deposited By: Andert, Dr.-Ing. Franz
Deposited On:01 Sep 2020 12:19
Last Modified:01 Sep 2020 12:19

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