Action-driven Reinforcement Learning for Improving Localization of Brace Sleeve in Railway Catenary

Junping Zhong, Zhigang Liu, Hongrui Wang, Wenqiang Liu, Cheng Yang, Alfredo Nunez

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
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Brace Sleeve (BS) plays an essential role in connecting and fixing cantilevers of railway catenary systems. It needs to be monitored to ensure the safety of railway operations. In the literature, image processing techniques that can localize BSs from inspection images are proposed. However, the boxes produced by existing methods can contain incomplete and/or irrelevant information of the localized BS. This reduces the accuracy of BS condition diagnosis in further analyses. To address this issue, this paper proposes the use of an action-driven reinforcement learning method that adopts the coarse-localized box provided by existing methods, and finds the movements needed for the box to approach to the true BS position automatically and accurately. In contrast to the existing methods that predict one position of the box containing a BS, the proposed action-driven method sees the localization problem as a dynamic position searching process. The localization of BS is achieved by following a sequence of actions, which in this paper are position-moving (up, down, left or right), scale-changing (scale up or scale down) and shape-changing (fatter or taller). The policy of selecting dynamic actions is obtained by reinforcement learning. In the experiment, the proposed method is tested with real-life images taken from a high-speed line in China. The results show that our method can effectively improve the localization accuracy for 81.8% of the analyzed images. We also analyze cases where the method did not improve the localization and suggest further research lines.
Original languageEnglish
Title of host publicationInternational Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2020 - Proceedings
Subtitle of host publicationProceedings
Number of pages6
ISBN (Electronic)978-1-7281-9277-2
ISBN (Print)978-1-7281-9278-9
Publication statusPublished - 2020
EventICSMD 2020: International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence - Xi'an, China
Duration: 15 Oct 202017 Oct 2020


ConferenceICSMD 2020

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • action-driven learning
  • brace sleeve
  • localization
  • railway catenary
  • reinforcement learning


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