Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Kou, Lei; Sysyn, Mykola; Fischer, Szabolcs; Liu, Jianxing; Nabochenko, Olga

doi:10.3390/s22218214

Cited by 19 publications

(8 citation statements)

References 41 publications

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“…Deep-learning-based algorithms are being widely used in industrial defect detection research in recent years due to their high efficiency and accuracy. Many researchers have devoted themselves to researching industrial defect detection algorithms based on supervised learning algorithms, which significantly depends on labeled defect data [ 24 , 25 , 26 , 27 , 28 , 29 ]. However, due to the hardship of collecting defective samples, it is extremely hard to obtain enough defect data for a deep model to learn its distribution.…”

Section: Related Workmentioning

confidence: 99%

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

Min,

Wang,

Liu

et al. 2023

Sensors

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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 simple yet effective rail surface defect detection model, FS-RSDD, for rail surface condition monitoring, which also aims to address the issue of insufficient defect samples faced by previous detection models. The model utilizes a pre-trained model to extract deep features of both normal rail samples and defect samples. Subsequently, an unsupervised learning method is employed to learn feature distributions and obtain a feature prototype memory bank. Using prototype learning techniques, FS-RSDD estimates the probability of a test sample belonging to a defect at each pixel based on the prototype memory bank. This approach overcomes the limitations of deep learning algorithms based on supervised learning techniques, which often suffer from insufficient training samples and low credibility in validation. FS-RSDD achieves high accuracy in defect detection and localization with only a small number of defect samples used for training. Surpassing benchmarked few-shot industrial defect detection algorithms, FS-RSDD achieves an ROC of 95.2% and 99.1% on RSDDS Type-I and Type-II rail defect data, respectively, and is on par with state-of-the-art unsupervised anomaly detection algorithms.

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Section: Related Workmentioning

confidence: 99%

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

Min,

Wang,

Liu

et al. 2023

Sensors

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“…In paper [ 26 ] it was shown that the curvature graphs of the axis of an exploited railway track clearly differ from the graphs obtained for model layouts; they have a less regular, oscillatory character, which results from measurement error and the deformations of the ballasted track [ 32 , 33 , 34 ]. However, this did not prevent the basic geometrical parameters of the measured layout from being estimated.…”

Section: Determination Of the Curvature Values For The Test Sectionmentioning

confidence: 99%

The Procedure of Identifying the Geometrical Layout of an Exploited Railway Route Based on the Determined Curvature of the Track Axis

Koc

2022

Sensors

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This paper presents a detailed algorithm for determining the curvature of a track axis with the use of a moving chord method, and then discusses the procedure for identifying the geometric layout of an exploited railway route on the basis of the determined curvature. In the moving chord method, the measured coordinates of the track axis allow one to directly determine the existence of the horizontal curvature without the need for additional measurements. This enables comprehensively identifying the existing geometric elements—straight lines, circular arcs, and transition curves. The conducted activities were illustrated with a calculation example, including a 5.5 km long test section with five areas of directional change. This showed a sequential procedure that led to the solution of the given problem. Based on the curvature diagram, the coordinates of the segmentation points, which define the connections of the existing geometric elements with each other, were determined.

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“…Therefore, the semantic segmentation method is the most effective method for rail surface defect detection. Kou et al [23] developed a fast and cost-effective method for rail surface defect detection using only a low-cost camera through deep learning and semantic segmentation techniques, achieving accuracy comparable to magnetic particle inspection, with the potential to further improve detection speed using high-frequency cameras. Aiming at the problems of small defects and an insufficient number of samples in the detection of rail surface defects, He et al [24] introduced a deformable convolution and attention mechanism in the FPN network to improve the model's ability to detect defects at different scales, and they utilized the migration learning strategy to perform the feature extraction in the new network architecture.…”

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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Optical Rail Surface Crack Detection Method Based on Semantic Segmentation Replacement for Magnetic Particle Inspection

Cited by 19 publications

References 41 publications

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

The Procedure of Identifying the Geometrical Layout of an Exploited Railway Route Based on the Determined Curvature of the Track Axis

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

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