Railway fastener maintenance is now more accurate and simpler than ever!

Railway fastener maintenance is now more accurate and simpler than ever!

Railway fasteners are critical equipment since they keep rails fixed. they should be routinely monitored to guarantee the safety of rail lines. The monitoring process performed by trained staff is time-consuming, boring, dangerous, costly, and plagued with human error, especially for long railways.  To tackle these issues, several computer vision algorithms have been developed to control railway lines rapidly and automatically. However, they mostly ignore the fact that fasteners might be covered by stones or that their shapes can be significantly different under actual working conditions. Thus, their results suffer from a high false detection rate. Additionally, various types of fasteners with different features are used in the railroad industry which makes their classification challenging. Another issue is the standardization of images captured by cameras installed on trains. As the weather conditions change, the brightness of the obtained images varies substantially, leading to wrong predictions by AI models. Recently, Yang Ou et al. from the mechanical engineering school of the Southwest Jiaotong University in China proposed a new computer vision model based on the Bayesian hierarchical model (FSM) for classification assuming four different conditions for fasteners: normal (N), partially worn (P), missing (M), and covered (C) (Figure 1). They labeled both training and test datasets using a discriminative model called CRF for automatic image segmentation. They employed the support vector machine (SVM) algorithms to analyze obtained fastener images.