Detection Of Rail Surface Defects Research Articles

Deep Learning (Fast R-CNN)-Based Evaluation of Rail Surface Defects

In current railway rails, trains are propelled by the rolling contact between iron wheels and iron rails, and the high frequency of train repetition on rails results in a significant load exertion on a very small area where the wheel and rail come into contact. Furthermore, a contact stress beyond the allowable stress of the rail may lead to cracks due to plastic deformation. The railway rail, which is the primary contact surface between the wheel and the rail, is prone to rolling contact fatigue cracks. Therefore, a thorough inspection and diagnosis of the condition of the cracks is necessary to prevent fracture. The Detailed Guideline on the Performance Evaluation of Track Facilities in South Korea specifies the detailed requirements for the methods and procedures for conducting track performance evaluations. However, diagnosing rail surface damage and determining the severity solely rely on visual inspection, which depends on the qualitative evaluation and subjective judgment of the inspector. Against this backdrop, rail surface defect detection was investigated using Fast R-CNN in this study. To test the feasibility of the model, we constructed a dataset of rail surface defect images. Through field investigation, 1300 images of rail surface defects were obtained. Aged rails collected from the field were processed, and 1300 images of internal defects were generated through SEM testing; therefore, a total of 1300 pieces of learning data were constructed. The detection results indicated that the mean average precision was 94.9%. The Fast R-CNN exhibited high efficiency in detecting rail surface defects, and it demonstrated a superior recognition performance compared with other algorithms.

ISRM: introspective self-supervised reconstruction model for rail surface defect detection and segmentation

The problems of intrinsic imbalance of the sample and interference from complex backgrounds limit the performance of existing deep learning methods when applied to the detection and segmentation of rail surface defects. To address these issues, an introspective self-supervised reconstruction model (ISRM) is proposed, which only requires normal samples in the training phase and incorporates the concept of self-supervised learning into an introspective autoencoder. The training framework of ISRM first extracts general features using a pretrained Feature Extractor. Subsequently, a Feature Transformer transfers the features to the target domain. Next, a synthetic defect embedder embeds Bessel-Gaussian random defects into the feature space. Finally, the asymmetric autoencoder reconstructs the rail surface features back into image space. The transformation of pretrained features into target-oriented features helps mitigate domain bias. Since defects exhibit higher commonality in the feature space relative to the image space, embedding synthetic defects into the feature space effectively improves training efficiency. Moreover, the adversarial training architecture enhances the clarity of reconstructed images. The impact of core parameters on the model performance is analyzed through ablation experiments. The results from comparative experiments demonstrate that ISRM achieves 98.5% and 97.2% accuracy on defect detection and segmentation tasks, respectively, reducing the error rate by 11.8% and 3.4% compared to the current state-of-the-art model.

Rail surface defect detection using a transformer-based network

The detection of Rail Surface Defects (RSDs) plays a critical role in railway track maintenance. Traditional image processing methods exhibit limitations due to their intricate design and insufficient robustness, thereby restricting their broader applications. Recently, deep learning-based RSD detection methods have drawn great attention. However, these methods predominantly rely on Convolutional Neural Networks (CNN), neglecting the hierarchical linkages amongst disparate features, which impedes the refined portrayal of RSDs. To address these issues, we propose RailFormer, a novel system leveraging the capabilities of Transformer-based networks for the precise and efficient detection of RSDs. The encoder in RailFormer incorporates overlapped patch merging, efficient self-attention, and a Mix-feed Forward Network (FFN), all meticulously designed to bolster feature fusion from both global and local perspectives. Additionally, we have implemented a Criss-Cross attention module within the decoder to facilitate RSD detection and manage computational complexity. In this study, the proposed RailFormer and four other models including SegFormer, Swin Transformer, ViT, and UNet are trained and compared. We employ the widely used public RSD datasets RSDD, encompassing both Type-I and Type-II RSDD images and a customized RSD dataset, as a basis for performance comparison. The training outcomes and visualization results show that RailFormer achieves the highest mean Intersection over Union (mIoU) and superior visualization performance on the RSDD and the customized RSD datasets. These results demonstrate the superiority of RailFormer and underline its potential for future deployment in railway track inspection applications.

An Improved YOLOv8 Algorithm for Rail Surface Defect Detection

An Improved YOLOv8 Algorithm for Rail Surface Defect Detection

Resolving mode mixing in wheel–rail surface defect detection using EMD based on binary time scale

Due to the mode mixing, empirical mode decomposition (EMD) cannot effectively decompose the vibration signal when the signal is intermittent and pulse interference caused by discontinuous vibration. The methods to solve mode mixing often use noise assistance, such as ensemble EMD (EEMD), complete EEMD (CEEMD), etc. These methods can effectively solve mode mixing, but they also have shortcomings. In EEMD, the added noises not only have residual effects and time-consuming. The drawback of CEEMD is that it is difficult to align during set averaging. In this paper, an improved EMD based on binary time scale (EMD-BTS) is proposed for the fault feature extraction of wheel–rail defect detection. Firstly, the generalized intrinsic mode function (GIMF) is defined based on the time-domain characteristics of non-stationary vibration signals. Then, to tackle the drawbacks of EMD which cannot effectively solve mode mixing caused by signal intermittence and pulse interference, the inherent mode is extracted in the EMD-BTS to decompose the raw signals into GIMFs. Finally, the false components generated by over decomposition are combined based on time-domain cross covariance. A simulation case and a actual case of vehicle bogie are utilized to verify the feasibility of the proposed EMD-BTS. The results indicate that the proposed approach exceeds other typical techniques in extracting intermittent fault features of wheel–rail defect detection.

Self-Supervised Defect Representation Learning for Label-Limited Rail Surface Defect Detection

Self-Supervised Defect Representation Learning for Label-Limited Rail Surface Defect Detection

A Signal Filtering Method for Magnetic Flux Leakage Detection of Rail Surface Defects Based on Minimum Entropy Deconvolution

Magnetic flux leakage (MFL) detection of rail surface defects is an important research field for railway traffic safety. Due to factors such as magnetization and material, it can generate background noise and reduce detection accuracy. To improve the detection signal strength and enhance the detection rate of more minor defects, a signal filtering method based on minimum entropy deconvolution is proposed to denoise. By using the objective function method, the optimal inverse filter parameters are calculated, which are applied to the filtering detection of MFL signals of the rail surface. The detection results show that the peak-to-peak ratio of the defect signal and noise signal detected by this algorithm is 2.01, which is about 1.5 times that of the wavelet transform method and median filtering method. The defect signal is significantly enhanced, and the detection rate of minor defects on the rail surface can be effectively improved.

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

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.

Unsupervised Pixel-Level Detection of Rail Surface Defects Using Multistep Domain Adaptation

Detection of rail surface defects using deep learning largely relies on supervision with pixel-level ground truths, where the detection accuracy suffers due to unseen image domains. In order to streamline the laborious labeling process and accurately identify defects on rail surfaces a U-Net-based network that integrates spatial and channel attention (SA) modules, multistep domain adaptation (MSDA), and progressive histogram matching (PHM) is developed in the context of pixel-level defects detection in this research. MSDA with an unlabeled intermediate dataset is used to reduce the domain gap underlying different domains. To further improve the adaptation of the transferred model, the PHM method and the self-training method are employed to enhance detection accuracy. Trained in the source domain with ground truths, the deep learning model is then tested in the target domain with no annotations. Experimental results on the RSDD dataset and the Rail-Joint dataset demonstrate the applicability and effectiveness of the proposed approach. By alleviating the domain discrepancy, the developed model drastically enhances the average precision (AP) score of unsupervised detection from 0.103 to 0.861, which shows competitive performance compared with the AP score of 0.895 by the state-of-the-art supervised models. The novelty of this research lies in the development of a hybrid deep learning approach that is able to achieve competitive detection accuracy with a low overlap ratio between domains and few data labeling.

Automatic Rail Surface Defect Inspection Using the Pixelwise Semantic Segmentation Model

Rail surface defect (RSD) is an important railroad track quality indicator and impacts the overall track safety and ride quality. Railroads need to inspect and evaluate RSD conditions regularly to make proper maintenance plan for both freight and passenger services. Unfortunately, current field practices for rail surface defect defection have not been fully automated yet and significant amount of manual inspections are still involved, which could be labor-intensive but low-efficient. Earlier efforts have proposed multiple automatic rail surface defect detection systems, but the accuracy and equipment cost issues have limited their applications in the field. To address the needs in track inspections, this study proposes an improved Deeplabv3-plus model using a lightweight backbone, attention module, and the Lovász-Softmax loss to automatically detect RSD. The improved model not only improves the detection performance but also keeps the computational cost manageable. The ResNet-18 backbone is adopted for the encoder design to reduce the computational cost and maximize the inference speed. The CBAM attention module is unified with the decoder structure for critical features’ representation. The optimum crop size is determined in the training process. The Lovász-Softmax loss is implemented to address the severe class imbalance issue. The improved DeepLabv3-plus model is compared with five other models to validate the performance. Experimental results on both original data and noisy data confirm the proposed model achieves the best performance on evaluation metrics and visualization. This work provides a feasible solution for the future implementation of automatic railroad track inspection.

Rail Surface Defect Detection Based on An Improved YOLOv5s

As the operational time of the railway increases, rail surfaces undergo irreversible defects. Once the defects occur, it is easy for them to develop rapidly, which seriously threatens the safe operation of trains. Therefore, the accurate and rapid detection of rail surface defects is very important. However, in the detection of rail surface defects, there are problems, such as low contrast between defects and the background, large scale differences, and insufficient training samples. Therefore, we propose a rail surface defect detection method based on an improved YOLOv5s in this paper. Firstly, the sample dataset of rail surface defect images was augmented with flip transformations, random cropping, and brightness transformations. Next, a Conv2D and Dilated Convolution(CDConv) module was designed to reduce the amount of network computation. In addition, the Swin Transformer was combined with the Backbone and Neck ends to improve the C3 module of the original network. Then, the global attention mechanism (GAM) was introduced into PANet to form a new prediction head, namely Swin transformer and GAM Prediction Head (SGPH). Finally, we used the Soft-SIoUNMS loss to replace the original CIoU loss, which accelerates the convergence speed of the algorithm and reduces regression errors. The experimental results show that the improved YOLOv5s detection algorithm reaches 96.9% in the average precision of rail surface defect detection, offering the accurate and rapid detection of rail surface defects, which has certain engineering application value.

Rail Surface Defect Detection Based on Image Enhancement and Improved YOLOX

During the long and high-intensity railway use, all kinds of defects emerge, which often produce light to moderate damage on the surface, which adversely affects the stable operation of trains and even endangers the safety of travel. Currently, models for detecting rail surface defects are ineffective, and self-collected rail surface images have poor illumination and insufficient defect data. In light of the aforementioned problems, this article suggests an improved YOLOX and image enhancement method for detecting rail surface defects. First, a fusion image enhancement algorithm is used in the HSV space to process the surface image of the steel rail, highlighting defects and enhancing background contrast. Then, this paper uses a more efficient and faster BiFPN for feature fusion in the neck structure of YOLOX. In addition, it introduces the NAM attention mechanism to increase image feature expression capability. The experimental results show that the detection of rail surface defects using the algorithm improves the mAP of the YOLOX network by 2.42%. The computational volume of the improved network increases, but the detection speed can still reach 71.33 fps. In conclusion, the upgraded YOLOX model can detect rail surface flaws with accuracy and speed, fulfilling the demands of real-time detection. The lightweight deployment of rail surface defect detection terminals also has some benefits.

An Improved Feature Pyramid Network and Metric Learning Approach for Rail Surface Defect Detection

When deep learning methods are used to detect rail surface defects, the training accuracy declines due to small defects and an insufficient number of samples. This paper investigates the problem of rail surface defect detection by using an improved feature pyramid network (FPN) and the metric learning approach. Firstly, the FPN is improved by adding deformable convolution and convolutional block attention modules to improve the accuracy of detecting defects of different scales, and it is pretrained on the MS COCO dataset. Secondly, a new model is established to extract network features based on the transfer learning model and learned network parameters. Thirdly, a multimodal network structure is constructed, and the distance between each modal representative and the embedded feature vector is calculated to classify the defects. Finally, experiments are carried out on the miniImageNet dataset and the rail surface defect dataset. The results show that the mAP (five-way five-shot) of our method is 73.42% on the miniImageNet dataset and 63.29% on the rail defect dataset. Our experiments show the effectiveness of the proposed method, and the results of the rail surface defect detection are satisfactory. As there are few sample classification studies of rail surface defects, this work provides a different approach and lays a foundation for further research.

Rail Surface Defect Detection and Location Based on Image Processing

Rail Surface Defect Detection and Location Based on Image Processing

Detection of Rail Surface Defects Based on Ensemble Learning of YOLOv5

Demiryolu ulaşımı son yıllarda demiryolu hat uzunluğunun artmasıyla beraber kapasitesini arttırmıştır. Hızlı trenlerin gelişmesi de bu duruma katkı sağlamıştır. Yolcu ve yük kapasitesinin artması güvenlik tedbirlerinin önemini daha da arttırmıştır. Demiryolu hatlarının güvenliğini sağlamak için hatların belirli aralıklarla denetlenmesi gerekmektedir. Demiryolu hattı bakımında ray üzerinde bulunan kusurların tespiti son derece önemlidir. Bu çalışmada demiryolu bakımının önemli bir parçası olan ray bileşeni üzerindeki kusurların tespitine odaklanılmıştır. Çalışmada ray üzerinde bulunan kusurları bir nesne tespiti yöntemi olan YOLO ile tespit etme yoluna gidilmiştir. Farklı YOLO modelleri için topluluk öğrenmesine dayalı bir yöntem önerilmiştir. Deney sonuçları, 8 farklı kusur içeren veri seti üzerinde bütün sınıfları içeren tespit oranının %80’in üzerinde olduğunu göstermiştir.

Rail Surface Defect Detection Through Bimodal RSDINet and Three-Branched Evidential Fusion

Current state-of-the-art intensity and depth modal-based railway surface defect inspection system faces the dilemma between false alarming and miss detection. To overcome this challenge, a bimodal detection scheme using both feature-level fusion and (evidence theory-based) decision-level fusion is designed. Moreover, an improved evidential fusion algorithm is proposed, which adopts three-branched evidential weight structure and introduces the Transferable Belief Model to the improved decision functions, achieving outstanding performance on effectiveness and robustness analysis. Both theoretical justifications and experimental results validate the efficiency of the hybrid fusion-based detection scheme in solving rail surface defect problems.

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

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

Deep Learning and Laser-Based 3-D Pixel-Level Rail Surface Defect Detection Method

Rail surface defect inspection is of particular importance in modern railways. Accurate and efficient surface defect detection approaches support optimized maintenance. This enables safe operation of the railway network. However, the scale and harsh working environments of the railway still pose challenges to existing manual and vision-based inspection methods. Inspired by recent advances in laser measurement and deep learning in computer vision, this paper proposes a laser-based 3D pixel-level rail surface defect detection method that combines high-precision laser measurement data with the concept of deep semantic segmentation. In the proposed method, the rail surface is first measured in 3D using a low-cost 2D laser triangulation sensor. Then, a new deep semantic segmentation network is introduced. The network is composed of a fully convolutional segmentation module and two symmetric mapping modules, which can take 3D laser measurement data as input and output 3D pixel-level defect detection results in an end-to-end manner. The modular design of the network allows the use of various segmentation modules for different applications or scenarios. Experiments on a 3D rail dataset demonstrate the feasibility of the proposed method with a pixel-level detection accuracy measured by mean Intersection over Union (mIoU) of up to 87.9%. The 3D output provides not only location and boundary information but also the 3D characterization of defects, giving an essential reference for further defect management and repair tasks.

CSANet: Contour and Semantic Feature Alignment Fusion Network for Rail Surface Defect Detection

CSANet: Contour and Semantic Feature Alignment Fusion Network for Rail Surface Defect Detection

TSSTNet: A Two-Stream Swin Transformer Network for Salient Object Detection of No-Service Rail Surface Defects

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 and form of the defects are often very similar to the background, which makes them difficult for the human eye to distinguish. How to accurately identify rail surface defects thus poses a challenge. We introduce salient object detection through machine vision to deal with this challenge. Salient object detection locates the most “significant” areas of an image using algorithms, which constitute an integral part of machine vision inspection. However, existing saliency detection networks suffer from inaccurate positioning, poor contouring, and incomplete detection. Therefore, we propose an innovative deep learning network named Two-Stream Swin Transformer Network (TSSTNet) for salient detection of no-service rail surface defects. Specifically, we propose a two-stream encoder—one stream for feature extraction and the other for edge extraction. TSSTNet also includes a three-stream decoder, consisting of a saliency stream, edge stream, and fusion stream. For the problem of incomplete detection, we innovatively introduce the Swin Transformer to model global information. For the problem of unclear contours, we expect to deepen the understanding of the difference in depth between the foreground and background through the learning of contour maps, so the contour alignment module (CAM) is created to deal with this problem. Moreover, to make the most of multimodal information, we suggest a multi-feature fusion module (MFFM). Finally, we conducted comparative experiments with 10 state-of-the-art (SOTA) approaches on the NRSD-MN datasets, and our model performed more competitively than others on five metrics.