OPENnet: Object Position Embedding Network for Locating Anti-Bird Thorn of High-Speed Railway

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  • WANG Zhuo
    School of Computer and Information Technology, Beijing Jiaotong University
  • LIU Junbo
    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited
  • WANG Fan
    Infrastructure Inspection Research Institute, China Academy of Railway Sciences Corporation Limited
  • WU Jun
    School of Computer and Information Technology, Beijing Jiaotong University

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<p>Machine vision-based automatic anti-bird thorn failure inspection, instead of manual identification, remains a great challenge. In this paper, we proposed a novel Object Position Embedding Network (OPENnet), which can improve the precision of anti-bird thorn localization. OPENnet can simultaneously predict the location boxes of the support device and anti-bird thorn by using the proposed double-head network. And then, OPENnet is optimized using the proposed symbiotic loss function (SymLoss), which embeds the object position into the network. The comprehensive experiments are conducted on the real railway video dataset. OPENnet yields competitive performance on anti-bird thorn localization. Specifically, the localization performance gains +3.65 AP, +2.10 AP50, and +1.22 AP75.</p>

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