Deep neural networks have greatly improved the performance of rail surface defect
segmentation when the test samples have the same distribution as the training samples …

Deep learning techniques have largely solved the problem of rail surface defect detection
(SDD), however, two aspects have yet to be addressed. In most existing approaches, two …

Deep neural network has demonstrated high-level accuracy in rail surface defect
segmentation. However, deploying these deep models in actual inspection situations results …

Owing to the development of convolutional neural networks (CNNs), the detection of defects
on rail surfaces has significantly improved. Although existing methods achieve good results …

The detection of no-service rail surface defects is important in the rail manufacturing
process. Detection of defects can prevent significant financial losses. However, the texture …

The increasing use of high-pressure hydrogen gas has heightened the need to understand
material behavior in hydrogen-rich environments. Recent studies have shown that …

Recently, scene coordinate regression (SCoRe) approaches for visual localization have
been extensively investigated. However, current SCoRe methods are scene-specific and …

With digital information applied in intelligent transportation system, pavement crack
detection with digital twins has drawn widely attention since the past several years …

This paper provides an overview of the applications of Convolutional Neural Networks
(CNN) in the railway maintenance industry. Our research covers specifically the subdomain …

As an important component of the railway system, the surface damage that occurs on the
rails due to daily operations can pose significant safety hazards. This paper proposes a …