A classification model of railway fasteners based on computer vision

Abstract

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.