Neural Computing and Applications

, Volume 31, Issue 12, pp 9307–9319 | Cite as

A classification model of railway fasteners based on computer vision

  • Yang Ou
  • Jianqiao Luo
  • Bailin LiEmail author
  • Biao He
Original Article


Fasteners are critical railway components that maintain the rails in a fixed position. The state of fasteners needs to be periodically checked in order to ensure safe transportation. Several computer vision methods have been proposed in the literature for fastener classification. However, these methods do not take into consideration the fasteners covered by stone. This paper proposes a new fastener classification model, which can divide fasteners into four types, including normal, partially worn, missing, and covered. First, the traditional latent Dirichlet allocation is introduced for fastener classification and its shortcomings are analyzed. Second, conditional random fields are used to segment the fastener structure. Third, the Bayesian hierarchical model of fastener feature words and structure labels is established. Then, the topics hidden behind the fastener feature words are derived, and the fastener image is ultimately represented by a topic distribution. Finally, the fasteners are classified using the support vector machine. The experimental results demonstrate the effectiveness of this method.


Fastener classification Structure labels Latent Dirichlet allocation (LDA) Conditional random fields (CRF) 



Latent Dirichlet allocation


Conditional random fields


Spatial pyramid LDA




Generalized linear model


Maximum a posteriori


Maximum posterior marginal


Tree-reweighted belief propagation


Supervised LDA



This work is supported by Sichuan Province Science and Technology Support Program under grant 2018GZ0361.

Authors’ contributions

We propose a new fastener classification model, which can divide fasteners into four types, including normal, partially worn, missing, and covered. All authors read and approved the final manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringSouthwest Jiaotong UniversityChengduChina

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