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

Zhuo WANG, Junbo LIU, Fan WANG, Jun WU

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E106-D No.5 pp.824-828
Publication Date
2023/05/01
Publicized
2022/11/14
Online ISSN
1745-1361
DOI
10.1587/transinf.2022DLL0011
Type of Manuscript
Special Section LETTER (Special Section on Deep Learning Technologies: Architecture, Optimization, Techniques, and Applications)
Category
Intelligent Transportation Systems

Authors

Zhuo WANG
  Beijing Jiaotong University
Junbo LIU
  China Academy of Railway Sciences Corporation Limited
Fan WANG
  China Academy of Railway Sciences Corporation Limited
Jun WU
  Beijing Jiaotong University

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