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Locator slope calculation via deep representations based on monocular vision

  • Yang YangEmail author
  • Wensheng Zhang
  • Zewen He
  • Dongjie Chen
Original Article
  • 126 Downloads

Abstract

The locator is the key component to control the track of contact wire in overhead catenary system (OCS) for high-speed railway. Once the locator slope is out of bound, it would pose a huge hazard to the safety of the high-speed trains and threat to the human life and property damage. In this work, a novel end-to-end locator slope calculation framework is presented for locator slope real-time inspection in high-speed railway system. The pipeline is composed of two stages: locator contour detection and slope calculation. In order to precisely detect the locator contours in OCS images captured from high-speed extreme environments, a novel detection mechanism including rough detection and fine fitting is proposed. For the fast slope calculation through only one camera, monocular vision model is modified by two novel assumptions to calculate the locator space coordinates. Rigorous experiments are performed across a number of standard locator slope calculation benchmarks, showing a large improvement in the precision and speed over all previous methods. Finally, the effectiveness of proposed framework is demonstrated through a real-world application of the high-speed rail OCS inspection system.

Keywords

Slope calculation Locator detection Convolution neural networks Monocular vision 

Notes

Acknowledgements

The authors would like to thank all the research scholars, M.Tech. students and technical staffs for their comments and suggestions. At the same time, the research was supported in part by Natural Science Foundation of China (Nos. 61602484, 61432008, 61472423 and U1636220).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest in this manuscript

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina

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