Rail base flaw detection and quantification based on the modal curvature method and the back propagation neural network

Liu, Yan; Li, Qiutong; Yuan, Xianpu; Li, Yixin; Zhu, Zhehao

doi:10.1016/j.engfailanal.2022.106792

Cited by 3 publications

(1 citation statement)

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“…The method analyzes vibration signals collected by piezoelectric ceramic pads and employs deep learning techniques. Li et al [20] investigated the application of the modal curvature method and neural network technology to the often-neglected problem of track substrate defects, and they developed a new algorithm for detecting and quantifying track substrate defects, which was demonstrated through numerical simulations and experimental validation to show its effectiveness in both free and fixed-track inspection. Liu et al [21] proposed a deep convolutional neural-networkbased transfer learning (DCTL) approach to achieve effective damage identification in the health monitoring of sharp rail structures through affine transform data enhancement using a pre-trained Inception-ResNet-V2 model and a 1D signal-to-2D image conversion technique, and it showed higher performance than the traditional approach in the experiments.…”

Section: Introductionmentioning

confidence: 99%

Rail-STrans: A Rail Surface Defect Segmentation Method Based on Improved Swin Transformer

Si,

Luo,

Han

et al. 2024

Applied Sciences

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With the continuous expansion of the transport network, the safe operation of high-speed railway rails has become a crucial issue. Defect detection on the surface of rails is a key part of ensuring the safe operation of trains. Despite the progress of deep learning techniques in defect detection on the rails’ surface, there are still challenges related to various problems, such as small datasets and the varying scales of defects. Based on this, this paper proposes an improved encoder–decoder architecture based on Swin Transformer network, named Rail-STrans, which is specifically designed for intelligent segmentation of high-speed rail surface defects. The problem of a small and black-and-white rail dataset is solved using self-made large and multiple rail surface defect datasets through field shooting, data labelling, and data expansion. In this paper, two Local Perception Modules (LPMs) are added to the encoding network, which helps to obtain local context information and improve the accuracy of detection. Then, the Multiscale Feature Fusion Module (MFFM) is added to the decoding network, which helps to effectively fuse the feature information of defects at different scales in the decoding process and improves the accuracy of defect detection at multiple scales. Meanwhile, the Spatial Detail Extraction Module (SDEM) is added to the decoding network, which helps to retain the spatial detail information in the decoding process and further improves the detection accuracy of small-scale defects. The experimental results show that the mean accuracy of the semantic segmentation of the method proposed in this paper can reach 90.1%, the mean dice coefficient can reach 89.5%, and the segmentation speed can reach 37.83 FPS, which is higher than other networks’ segmentation accuracy. And, at the same time, it can achieve higher efficiency.

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Section: Introductionmentioning

confidence: 99%