Search Results (79)

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. Full article

Aiming at the core issues of the traditional YOLO11n model in rail surface defect detection—fine-grained feature loss of small defects, insufficient micro-target recognition accuracy, and the mismatch of existing downsampling/fusion methods for micro-defect feature extraction—this paper proposes an improved YOLO11n algorithm with two-dimensional network structure innovations. First, the Adaptive Downsampling (ADown) module is introduced into the backbone network for the first time, retaining global features via 2D average pooling and extracting local details through channel-split multi-path convolution/max pooling to avoid fine texture loss. Second, the original SOEP-RFPN-MFM neck network is designed, integrating SNI, GSConvE and MFM modules to achieve dynamic weighted fusion of multi-scale features and break the bottleneck of inefficient small-target feature aggregation. Trained and verified on a 4020-image rail dataset covering four defect types (Spalling, Squat, Wheel Burns, Corrugation), the improved algorithm achieves 93.7% detection accuracy, 92.4% recall and 95.6% mAP, realizing incremental improvements of 1.2, 2.6 and 0.8 percentage points, respectively, compared with the original YOLO11n, which is particularly optimized for rail micro-defect detection scenarios. This study provides a new deep learning method for rail transit micro-defect detection and a reference for scenario-specific improvement of lightweight YOLO11n models. Full article

In this study, with the rapid development of the field of rail vehicles, the laser welding process with high energy and small thermal deformation is selected, which reduces the working hours of post-welding grinding, repainting, and other processes, and ensures the industrial design requirements of the beautiful body after welding. The welding process for the non-penetration laser lap welding of SUS301L stainless-steel plates was optimized to address the problem of welding marks on the outer surface of railway vehicle car bodies. The impact of laser power, welding speed, and defocusing amount on weld penetration and tensile shear load was investigated using the response surface methodology. The results showed that the optimal response model for tensile shear load was the linear model, while the optimal response model for weld penetration was the 2FI model. The defocusing amount had the greatest influence on tensile shear load and weld penetration. When the laser power was 1.44 kW, the welding speed was 15 mm/s, and the defocusing amount was −4 mm, the tensile shear load reached its maximum by prediction. The actual tensile shear load of welded joints using these parameters was 4293 N with an error of merely 0.31% relative to the predicted value. The shear strength of laser-welded joints was measured at 429.3 N/mm, meeting the criteria established by the relevant standards. The tensile fracture shows characteristics of brittle fracture. The surface of the welded joints was bright white and well-formed, while the back side of the lower plate exhibited no signs of melting or welding marks. The microstructure of the weld zone (WZ) exhibited irregular columnar austenite and plate-like ferrite, while the heat-affected zone (HAZ) comprised columnar austenite and elongated bars or networks of δ-ferrite. The small-angle grain in welded joints can reduce grain boundary defects and mitigate stress concentration. After welding, angular deformation occurred, resulting in a residual stress distribution that shows tensile stress near the weld and compressive stress at a distance from the weld. Full article

Quick and reliable in situ non-destructive assessment of the material structure is especially critical in the case of measurement of rail defects concerning the demands of quick, uninterrupted transportation and safety. This paper presents the test results of a patented measuring head that is able to perform ultrasonic rail defect detection at speeds of up to 120 km/h. The experimental data was collected and discussed. Statistical analysis was performed in terms of bottom echo drop as a function of velocity, pressing force, and film thickness between the sensor and the rail material surface, as well as the coupling fluid stream intensity. The results proved the feasibility of the device for usage at high speeds for the state monitoring of rails in service. Full article

In this study, a small core diameter single mode fiber laser was applied to weld an 8 mm thick plate of 6061-T6 aluminum alloy. The microstructural evolution and mechanical properties of the laser welded aluminum alloy specimens were investigated in detail. The results indicated that fully penetrated welded specimens, free of welding defects like porosity, melt sagging, and hot cracking could be achieved by optimizing the processing parameters through response surface methodology. The upper part of the fusion zone consisted mainly of fine equiaxed dendrites, with secondary dendrite arm spacing (SDAS) of approximately 3–5 μm. While the lower region of the fusion zone exhibited pronounced microstructural coarsening, made up mostly of coarse columnar grains, along with some localized equiaxed grains, and an SDAS ranging from 8 to 12 μm. Both the fusion zone and heat affected zone (HAZ) were characterized by a “softened” hardness profile. The fusion zone featured a narrow region with the lowest microhardness across the welded joint with the microhardness value reducing to ~72% of the base metal (BM). Meanwhile, the microhardness of the HAZ was ~87.4% of the BM. The ultimate tensile strength of laser welded specimens was ~243.6 MPa, amounting to approximately 78.3% of the base metal. This study provides a fresh approach for welding medium-thick aluminum alloy plate using a high-quality laser beam, even at the kilowatt level with a fiber laser, and it shows a strong promise for applications in light-alloy manufacturing sectors such as automotive, rail transportation, aerospace, and beyond. Full article

Fine-grained defects on rail surfaces are often inadequately detected by conventional vision-based object detection models in low-light environments. Although this problem can be mitigated by enhancing image brightness and contrast or employing deep learning-based object detectors, these methods frequently distort critical edge and texture information essential for accurate defect recognition. Herein, we propose a preprocessing framework that integrates two complementary modules, namely adaptive illumination enhancement (AIE) and EdgeSeal enhancement (ESE). AIE leverages contrast-limited adaptive histogram equalization and gamma correction to enhance local contrast while adjusting the global brightness distribution. ESE further refines defect visibility through morphological closing and sharpening, enhancing edge continuity and structural clarity. When integrated with the You Only Look Once v11 (YOLOv11) object detection model and evaluated on a rail defect dataset, the proposed framework achieves an ~7% improvement in mean average precision over baseline YOLOv11 and outperforms recent state-of-the-art detectors under diverse low-light and degraded-visibility conditions. The improved precision and recall across three defect classes (defects, dirt, and gaps) demonstrate the robustness of our approach. The proposed framework holds promise for real-time railway infrastructure monitoring and automation systems and is broadly applicable to low-light object detection tasks across other industrial domains. Full article

A quasi-static FEM framework for wheel–rail contact is presented, aimed at large parametric analyses including independently rotating wheel (IRW) configurations. Unlike half-space formulations such as CONTACT, the FEM approach resolves global deformations and strongly non-Hertzian geometries while remaining computationally tractable through three key features: (i) a tailored mesh transition around the contact patch, (ii) solver settings optimized for frictional contact convergence, and (iii) an integrated post-processing pipeline for creep forces, micro-slip, and wear. The model is verified against CONTACT, an established surface-discretization reference based on the Boundary Element Method (BEM), demonstrating close agreement in contact pressure, shear stress, and stick–slip patterns across the Manchester Contact Benchmark cases. Accuracy is quantified using error metrics (MAE, RMSE), with discrepancies analyzed in high-yaw, near-flange conditions. Compared with prior FEM-based contact models, the main contributions are: (i) a rigid–flexible domain partition, which reduces 3D computational cost without compromising local contact accuracy; (ii) a frictionless preconditioning step followed by friction restoration, eliminating artificial shear-induced deformation at first contact and accelerating convergence; (iii) an automated selection of the elastic slip tolerance (slto) based on frictional-energy consistency, ensuring numerical robustness; and (iv) an IRW-oriented parametrization of toe angle, camber, and wheel spacing. The proposed framework provides a robust basis for large-scale studies and can be extended to transient or elastoplastic analyses relevant to dynamic loading, curved tracks, and wheel defects. Full article

Detecting metal surface crack defects is of great significance for the safe operation of industrial equipment. However, most existing mainstream deep-object detection models suffer from complex structures, large parameter sizes, and high training costs, which hinder their deployment and application in frontline construction sites. Therefore, this paper optimizes the existing YOLO series head structure and proposes a lightweight detection head structure, ZoomHead, with lower computational complexity and stronger detection performance. First, the GroupNorm2d module replaces the BatchNorm2d module to stabilize the model’s feature distribution and accelerate the training speed. Second, Detail Enhanced Convolution (DEConv) replaces traditional convolution kernels, and shared convolution is adopted to reduce redundant structures, which enhances the ability to capture details and improves the detection performance for small objects. Next, the Zoom scale factor is introduced to achieve proportional scaling of the convolution kernels in the regression branch, minimizing redundant computational complexity. Finally, using the YOLOv10 and YOLO11 series models as baseline models, ZoomHead was used to replace the head structure of the baseline models entirely, and a series of performance comparison experiments were conducted on the rail surface crack dataset and NEU surface defect database. The results showed that the integration of ZoomHead effectively improved the model’s detection accuracy, reduced the number of parameters and computations, and increased the FPS, achieving a good balance between detection accuracy and speed. In the comparative experiment of the SOTA model, the addition of ZoomHead resulted in the model having the smallest number of parameters and the highest FPS, while maintaining the same mAP value as the SOTA model, indicating that the ZoomHead structure proposed in this paper has better comprehensive detection performance. Full article

Rail surface defects pose significant risks to the operational efficiency and safety of industrial equipment. Traditional visual defect detection methods typically rely on high-quality RGB images; however, they struggle in low-light conditions due to small, low-contrast defects that blend into complex backgrounds. Therefore, this paper proposes a novel defect segmentation method leveraging a dual-stream feature fusion network that combines polarization images with DeepLabV3+. The approach utilizes the pruned MobileNetV3 as the backbone network, incorporating a coordinate attention mechanism for feature extraction. This reduces the number of model parameters and enhances computational efficiency. The dual-stream module implements cascade and addition strategies to effectively merge shallow and deep features from both the original and polarization images. This enhances the detection of low-contrast defects in complex backgrounds. Furthermore, the CBAM is integrated into the decoding area to refine feature fusion and mitigate the issue of missing small-target defects. Experimental results demonstrate that the enhanced DeepLabV3+ model outperforms existing models such as U-Net, PSPNet, and the original DeepLabV3+ in terms of MIoU and MPA metrics, achieving 73.00% and 80.59%, respectively. The comprehensive detection accuracy reaches 97.82%, meeting the demanding requirements for effective rail surface defect detection. Full article

Heavy rolling contact fatigue (RCF) may be caused by wheel surface defects under the influence of rail corrosion, which threatens the operational safety of rail vehicles. To investigate the role of surface defects on wheel RCF damage under the influence of rail corrosion, a salt spray tester was used to corrode the rails, an impact testing machine was employed to create surface defects, and RCF tests were completed. The role of surface defects on wheel RCF damage was studied by monitoring the wheel defect surface and cross-section. The results indicate that the tendencies of the RCF crack extension of surface defects of different sizes are similar, and they all extend in a C-shape along the tangential force direction. However, the larger the defect size, the later the crack is initiated. The leading edge material is continuously squeezed into the defect by the tangential force, and a larger plastic deformation layer is formed, which causes the RCF at the leading edge to crack more severely. Meanwhile, under the effect of combined normal force and shear stress, the leading edge crack intersects with the middle edge crack, and the leading edge material is spalled off first. Wheel RCF damage and wear are aggravated by rail corrosion, the longer the corrosion time, the more serious the RCF damage and wear, and the earlier the material spalling time, the lower the fatigue life. Full article

With the vigorous development of speedy railway technology, steel rails, as an important structural infrastructure in speedy railways, play a crucial role in ensuring the safety of the entire speedy railway operation. A brand-new type of speedy rail inspection robot and its fault detection method are proposed to solve a number of problems, such as the difficulty and low accuracy of real-time online detection of rail defects and damage in speedy railways. The brand-new rail inspection robot is driven by two drive wheels and adopts a standard rail gauge of 1435 mm, which ensures its speedy and smooth operation on the track as well as accurate motion posture information. Firstly, 12 common types of surface damage of the rail head were analyzed and classified into five categories based on their damage characteristics. The motion state of the rail inspection robot under the five types of surface damage of the rail head was analyzed and subjected to kinematic analysis. This study analyzed the relationship between the distinctive types of damage and the motion posture of the robot during the inspection of the five types of damage. Finally, experimental tests were conducted, and it was found that the robot’s motion posture would undergo sudden changes when inspecting distinctive types of injuries; the highest error rate was 3%. The effectiveness of this method was verified through experiments, and the proposed new track detection robot can greatly improve the track detection efficiency of high-speed railways and has specific academic research value and practical application value. Full article

To address the challenges in existing multi-sensor data fusion methods for rail surface defect diagnosis, particularly their limitations in fully exploiting potential synergistic information among multimodal data and effectively bridging the semantic gap between heterogeneous multi-source data, this paper proposes a diagnostic approach based on a Progressive Joint Representation Graph Attention Fusion Network (PJR-GAFN). The methodology comprises five principal phases: Firstly, shared and specific autoencoders are used to extract joint representations of multimodal features through shared and modality-specific representations. Secondly, a squeeze-and-excitation module is implemented to amplify defect-related features while suppressing non-essential characteristics. Thirdly, a progressive fusion module is introduced to comprehensively utilize cross-modal synergistic information during feature extraction. Fourthly, a source domain classifier and domain discriminator are employed to capture modality-invariant features across different modalities. Finally, the spatial attention aggregation properties of graph attention networks are leveraged to fuse multimodal features, thereby fully exploiting contextual semantic information. Experimental results on real-world rail surface defect datasets from domestic railway lines demonstrate that the proposed method achieves 95% diagnostic accuracy, confirming its effectiveness in rail surface defect detection. Full article

This paper presents a novel wayside rail monitoring system for real-time detection and quantification of rail surface cracks with sub-millimeter precision. The core innovation lies in mounting piezoelectric transducers on the web of the rail—an unconventional and practical location that avoids interference with wheel passages while enabling continuous monitoring in real-world conditions. Moreover, to directly quantify crack depth, a customized signal processing pipeline is developed, employing surface acoustic waves (SAWs) and incorporating a parallel reference transducer pair mounted on an undamaged rail section for calibration. This auxiliary pair provides a real-time calibration baseline, improving measurement robustness and accuracy. The method is experimentally validated on rail samples and verified through metallographic analysis. This approach enables condition-based maintenance by improving detection accuracy and offers the potential to reduce operational costs and enhance railway safety. Full article

With the widespread application of high-speed and heavy-load railways, the real-time detection of track surface defects has become increasingly crucial. To address the challenges in rail defect detection, this study proposes the PerMSCA-YOLO model, which aims to overcome the limitations of traditional object detection models in multi-scale, small target, and complex background scenarios. By incorporating the lightweight FasterNet backbone network, a multi-scale convolutional attention module, and perceptual loss, the proposed model significantly enhances the detection accuracy and robustness of track defects. Experimental results show that PerMSCA-YOLO achieves an mAP@0.5 of 0.856, an F1-score of 0.79, and an inference frame rate of 142 FPS, demonstrating superior detection accuracy and real-time performance compared to other mainstream models like YOLOv8n. Furthermore, the model exhibits strong adaptability and efficiency when dealing with complex track defects, such as microcracks and corrosion patches, indicating its broad practical application potential. The innovative contribution of this research lies in its effective strategy for improving detection accuracy and real-time performance through multi-scale feature fusion and deep semantic alignment mechanisms, providing a solution that balances both precision and efficiency for defect detection in complex track environments, with substantial engineering application potential. Full article

This work presents the construction of a transformation for the identification of surface defects on rails, starting with the selection of elements from the matrix and the creation of different matrices. It further elaborates on the recursive formulation of the transformation and demonstrates that, regardless of the elements’ uniqueness, the sum of the transformed matrix remains equal to the sum of the original matrix. This study also addresses the handling of matrices with repeated elements and proves that the G-transformation preserves information, ensuring the integrity of data without any loss or redundancy. Full article