The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 747–754, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-747-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 747–754, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-747-2020

  25 Aug 2020

25 Aug 2020

AUTOMATING POWERLINE INSPECTION: A NOVEL MULTISENSOR SYSTEM FOR DATA ANALYSIS USING DEEP LEARNING

O. Kähler1, S. Hochstöger1, G. Kemper2, and J. Birchbauer1 O. Kähler et al.
  • 1Siemens AG Österreich, 8054 Graz, Austria
  • 2GGS GmbH, Speyer, Germany

Keywords: Multisensor system, powerline inspection, Lidar, artificial intelligence, deep learning, Digital Twin

Abstract. Powerline infrastructure provides the backbone for the electricity supply of industrial, administrative and private sectors. Its maintenance requires regular inspections, that are still largely carried out manually. In this work, we propose an automated inspection system instead. We review current inspection processes as a baseline, give an overview of relevant inspection criteria, propose a suitable multi-modal sensor system, and discuss methods to automate the inspection tasks. In our system, we particularly focus on the high-level organization of the sensor data and inspection results to form a Digital Twin of the power line, that allows operators to browse through the recorded data in a meaningful way and review the status of their powerline from the desk.