Rail Surface Defect Detection Based on Improved UPerNet and Connected Component Analysis

To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.

Rail RCF damage quantification and comparison for different damage models

There are several fatigue-based approaches that estimate the evolution of rolling contact fatigue(RCF) on rails over time and built to be used in tandem with multibody simulations of vehicle dynamics. However, most of the models are not directly comparable with each other since they are based on different physical models even though they shall predict the same RCF damage at the end.This article studies different approaches to quantifying RCF and puts forward a measure for the degree of agreement between them. The methodological framework studies various steps in the RCF quantification procedure within the context of one another, identifies the ‘primary quantification step’ in each approach and compares results of the fatigue analyses. In addition to this, two quantities—‘similarity’ and ‘correlation’—have been put forward to give an indication of mutual agreement between models.Four widely used surface-based and sub-surface-based fatigue quantification approaches with varying complexities have been studied. Different operational cases corresponding to a metro vehicle operation in Austria have been considered for this study. Results showed that the best possible quantity to compare is the normalized damage increment per loading cycle coming from different approaches. Amongst the methods studied, approaches that included the load distribution step on the contact patch showed higher similarity and correlation in their results.While the different approaches might qualitatively agree on whether contact cases are ‘damaging’ due to RCF, they might not quantitatively correlate with the trends observed for damage increment values.