CUFuse: Camera and Ultrasound Data Fusion for Rail Defect Detection

The speed and load capacity of trains are improving at an accelerating rate, which raises the standards for railway services' security criteria. The track's surface may progressively reveal varying degrees of defects because of the impacts of moisture, temperature, load, and other factors; if the flaws never addressed in a prompt period, the degree of defects would expand, significantly raising the probability of train service. The development of automatic RSDD (Rail Surface Defect Detection) has substantial practical and scientific implications. In this study, a multi-crack detection technique depends on Variational Autoencoder Diffusion Model (VAEDM), The introduction of VAEDM allows for the non-destructive detection of fastener and RSD. In order to produce hidden elements from images that have various cracks, VAEDM combines the multi-layer and a rail surface image encoder was introduced. Zero Shot- Divergence Asynchronous Reinforcement Learning (ZS-DARL), Policy-Gradient is used to establish the relationship between defects and non-defects. DARL transformer encoder is introduced for taking long-range dependences for FE (Feature Extraction) from detected objects images with various patterns and dimensions. The simulation outcomes on the open Railway Track Fault Detection (RTFD) and Rail Surface Defect Datasets (RSDDs) with rail surface defects are collected from rail tracks surface defect detection. Results are measured using the metrics like recall, precision, F-measure, and accuracy.

The rapid and accurate identification of rail surface defects is critical to the maintenance and operational safety of the rail. For the problems of large-scale differences in rail surface defects and many small-scale defects, this paper proposes a rail surface defect detection algorithm, RSDNet (Rail Surface Defect Detection Net), with YOLOv8n as the baseline model. Firstly, the CDConv (Cascade Dilated Convolution) module is designed to realize multi-scale convolution by cascading the cavity convolution with different cavity rates. The CDConv is embedded into the backbone network to gather earlier defect local characteristics and contextual data. Secondly, the feature fusion method of Head is optimized based on BiFPN (Bi-directional Feature Pyramids Network) to fuse more layers of feature information and improve the utilization of original information. Finally, the EMA (Efficient Multi-Scale Attention) attention module is introduced to enhance the network's attention to defect information. The experiments are conducted on the RSDDs dataset, and the experimental results show that the RSDNet algorithm achieves a mAP of 95.4% for rail surface defect detection, which is 4.6% higher than the original YOLOv8n. This study provides an effective technical means for rail surface defect detection that has certain engineering applications.

Defect detection method for rail surface based on line-structured light

Due to the rapid advances in railway industry, the rail surface defect detection task which inspects whether the rail is defective has become an increasingly critical issue. Detecting rails by an automatic and swift approach instead of present manual inspections enables the work more efficient and safe currently. In this paper, we propose a novel two-stage pipeline method for rail defect detection by localizing and classifying rail images. Specifically, in the first stage, we get the cropped images which focus on the rail part instead of the whole-original image by integrating traditional image processing methods. In the second stage, we put the cropped images into a fine-tuned convolution neural network (CNN) and extract part-level features for rail images classification. Especially, in the rail image defect detection scenario, we should take recall into account to some extent, so we propose a novel loss function to leverage both of them in the second stage. The results show that the proposed method has strong robustness and achieves practical performance in defect detection precision.

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.

With the rapid development of rail transit, the workload of track maintenance has increased, making the intelligent identification of rail surface defects crucial for improving detection efficiency. To address issues such as low defect detection accuracy, the loss of feature information due to single-path architecture backbones, and insufficient information interaction in existing rail defect detection methods, we propose a rail surface defect detection method based on dual-path feature fusion (DPF). This method initially employs a dual-path structure to separately extract low-level and high-level features. It then utilizes a combination of attention mechanisms and feature fusion techniques to integrate these features. By doing so, it preserves richer information and enhances detection accuracy and robustness. The experimental results demonstrate that the comprehensive performance of the proposed model is superior to mainstream algorithms.

Rail surface defect detection plays a critical role in the maintenance of the rail transportation system. Video analysis technology is a promising method to detect defects due to its low cost and effectiveness. Several attempts with hand‐craft features have been made to obtain the detection results by using traditional machine vision algorithms. However, these methods suffer from imprecise results due to challenging conditions, such as deteriorated and changeable lighting environment and various types of complex rail surface defects. Recently, classification methods with complex deep convolutional networks have become popular. Despite their high accuracy, these methods cannot meet the requirements of defects localisation and real‐time processing in practice. To solve these problems, this study proposes a novel object detection algorithm to detect rail defects. The net architecture of the proposed algorithm includes a backbone network using MobileNet and several novel detection layers with multi‐scale feature maps inspired by you only look once (YOLO) and feature pyramid networks. Two different architectures of MobileNet are used to estimate the performance of defects detection. The experimental results demonstrate the great potential of the proposed algorithm with fast inference speed and high accuracy in the industry.

The application of computer vision technology in defect detection of industrial products is a popular research direction in recent years. This article presents the pyramid feature convolutional neural network (CNN) for defect detection of rail surfaces. First, multi-scale feature maps are extracted based on the characteristics of defects and backgrounds by the pyramid feature extraction module (PFEM). Then the feature maps are input to a lightweight network consisting of a small number of parameters. The network is trained with only 40% data of the dataset using binary cross-entropy loss function and the intersection of union (IOU) loss function. In the experiment, the performance of the proposed method is evaluated using the rail surface defect dataset (RSDD) dataset by comparing it with other methods. The experimental results show that the segmentation performance and real-time performance of the proposed method are better than those of other methods.

A versatile method in the space domain to study short-wave rail undulatory wear caused by rail surface defects

차륜과 레일의 반복적인 구름접촉은 레일 표면결함을 유발하고, 레일 표면결함은 충격하중을 유발하여 소음 진동, 레일파단, 궤도파괴로 이어지고 심할 경우 열차사고(탈선)를 발생할 수 있다. 이러한 레일 표면결함을 제어하기 위한 방법으로 레일연마가 시행되고 있다. 본 연구는 KTX차륜과 UIC레일에서 발생하는 최대 접촉압력을 유한요소해석을 통해 산정하였고, 일반레일 및 열처리레일에 대한 구름접촉피로시험을 수행하여 접촉압력 및 반복횟수에 따른 레일표면 경화층 형성 경향을 분석하였으며, 누적통과톤수에 따라 고속철도 레일에서 발생하는 표면 경화층을 제거하여 건전한 레일표면을 유지하기 위해 0.2mm/2천만톤의 적정 레일연마량을 제안하였다. The rail surface defects which are generated on repeated rolling contact fatigue are getting increased according to high speed, high density, and minimum weight. In addition, Increasing noise and vibration are affected by these also impact load generated as well. Because of this phenomenon, more serious and critical damages were occurred. In fact, in order to control them, the rail grinding amount in Korea. This study evaluated how depth of hardening on rail surface is formed and suggested optimal rail grinding amount by RCF(rolling contact fatigue) test with generated contact pressure between KTX wheel and UIC60 rail by applying FEM analysis. Therefore, the amount was generated approximately 0.2mm/20MGT to maintain integrity of rail surface by getting rid of depth of hardening on rail according to rail accumulated passing tonnage.

Heavy-haul railways play a vital role in freight transportation, and the health of the rails directly impacts the safety and efficiency of railway operations. The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation, spalling and abrasion, threatening operational safety. To address the issue, this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways. First, complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features. Next, dynamic time warping is applied to align rail profile data and extract key geometric features. Then, the vibration features and profile features are fused using Relief-F to select the most discriminative features. Finally, a support vector machine is utilized for defect identification. Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects, with an accuracy of 96.4%.

As track components deteriorate, their interaction dynamic response will alter accelerations on the train. Measuring acceleration on train components is a method to conduct condition monitoring of track defects. The measured signal can be used for the detection of rail surface defects which implement impacts on measured accelerations. In this paper, the axle-box acceleration (ABA) is measured in a subway as a case study. Fourier transform (FFT), empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods are used to study accelerations relative to the track. Wheel frequencies are calculated using finite element method (FEM) to determine frequency couplings. Velocity-dependent/independent components and source of excitation of the measured signal are distinguished. Results indicated that the FFT approach can be applied for both velocity-dependent and velocity-independent vibration components for frequencies up to 680 Hz. Also, the EEMD method can be used to distinguish the impact component of the measured signal.

The detection of rail surface defects is an important tool to ensure the safe operation of rail transit. Due to the complex diversity of track surface defect features and the small size of the defect area, it is difficult to obtain satisfying detection results by traditional machine vision methods. The existing deep learning-based methods have the problems of large model sizes, excessive parameters, low accuracy and slow speed. Therefore, this paper proposes a new method based on an improved YOLOv4 (You Only Look Once, YOLO) for railway surface defect detection. In this method, MobileNetv3 is used as the backbone network of YOLOv4 to extract image features, and at the same time, deep separable convolution is applied on the PANet layer in YOLOv4, which realizes the lightweight network and real-time detection of the railway surface. The test results show that, compared with YOLOv4, the study can reduce the amount of the parameters by 78.04%, speed up the detection by 10.36 frames per second and decrease the model volume by 78%. Compared with other methods, the proposed method can achieve a higher detection accuracy, making it suitable for the fast and accurate detection of railway surface defects.

To address the challenges of accurately extracting features from elongated scratches, irregular defects, and small-scale surface flaws on high-precision linear guide rails, this paper proposes a novel instance segmentation algorithm tailored for guide rail surface defect detection. The algorithm integrates the YOLOv8 instance segmentation framework with deformable convolutional networks and multi-scale feature fusion to enhance defect feature extraction and segmentation performance. A dedicated guide rail surface Defect (GSD) segmentation dataset is constructed to support model training and evaluation. In the backbone, the DCNv3 module is incorporated to strengthen the extraction of elongated and irregular defect features while simultaneously reducing model parameters. In the feature fusion network, a multi-scale feature fusion module and a triple-feature encoding module are introduced to jointly capture global contextual information and preserve fine-grained local defect details. Furthermore, a Channel and Position Attention Module (CPAM) is employed to integrate global and local features, improving the model’s sensitivity to channel and positional cues of small-target defects and thereby enhancing segmentation accuracy. Experimental results show that, compared with the original YOLOv8n-Seg, the proposed method achieves improvements of 3.9% and 3.8% in Box and Mask mAP50, while maintaining a real-time inference speed of 148 FPS. Additional evaluations on the public MSD dataset further demonstrate the model’s strong versatility and robustness.

In response to the low detection efficiency and accuracy of traditional manual methods for detecting road underground defects, this paper proposes an intelligent detection method based on ground penetrating radar (GPR). This method integrates the detection, classification, and localization of road underground defects. The approach uses Swin Transformer as a feature extraction network and utilizes the YOLOX object detection algorithm as a road underground defect detection model. It enables the detection of defect regions in three types of defect images: voids, non-compact areas, and underground pipelines. In addition, the collected radar signals are processed by Fourier transformation to obtain time-domain spectra and frequency-domain spectra, which are further analyzed to generate signal classification data set to achieve the defect classification. Finally, based on the relative positional relationship between the detected defect images and the GPS information collected by the GPR, the real positions of the defects on the map are automatically determined using the APIs provided by Amap (AutoNavi map). Experimental results show that this method achieves a precision and recall rate of 94.2% and 99.1%, respectively, for the detection of road underground defects, with an average precision of 94% and an average classification accuracy of 90%. The method significantly improves the accuracy and speed of road underground defect detection while meeting engineering requirements, making it highly valuable for practical road underground defect detection tasks.