A cyber-enabled visual inspection system for rail corrugation

Li, Qingyong; Shi, Zhiping; Zhang, Huayan; Tan, Yunqiang; Ren, Shuxin; Dai, Peng; Li, Weiyi

doi:10.1016/j.future.2017.04.032

Cited by 38 publications

(15 citation statements)

References 23 publications

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“…In the light of practical experience, when the setup of image acquisition equipment is fixed, the rail width w r in a rail image will be a constant. Therefore, the starting position of the rail is obtained based on the following rules [15]…”

Section: Rail Head Surface Localizationmentioning

confidence: 99%

“…Corrugation identification is one of the critical parts of this work and this subsection will evaluate the performance of the proposed method for corrugation identification. The proposed identification method is compared with three baselines including Gabor+SVM [13], Accumulate Energy Thresholding (AET) [14] and Maximum Energy (ME)+SVM [15]. More precisely, the global 8-dimensional Gabor filtering features and SVM classifier are adopted in Gabor+SVM.…”

Section: Performance Evaluation For Corrugation Identificationmentioning

confidence: 99%

“…It has become a widely used corrugation identification method based on computer vision [13] but it is sensitive to disturbances such as rust on the rail surface. Li et al proposed an identification method for rail corrugation based on rail image features in the frequency domain, including the image acquisition subsystem and the corrugation identification subsystem installed under the vehicle body [14,15]. Instead of global texture features, local frequency features used in this method can reduce the time-consuming problem of traditional image methods and further improve the identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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The Identification and Assessment of Rail Corrugation Based on Computer Vision

Wei

Liu

et al. 2019

Applied Sciences

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The identification and assessment of rail corrugation are two of the essential tasks of daily railway inspection to guarantee the safety of train operation and promote the development of an efficient maintenance strategy. In view of the requirements for automatic identification and smart decision-making, computer vision-based rail corrugation identification and assessment methods are presented in this paper. Firstly, an improved Spatial Pyramid Matching (SPM) model, integrating multi-features and locality-constrained linear coding (IMFLLC), is proposed for rail corrugation identification. After that, an innovative period estimation method for rail corrugation is proposed based on the frequency domain analysis of each column in the corrugation region. Finally, the severity of the rail corrugation is assessed with the help of the wear saliency calculation and fuzzy theory. The experiment results demonstrate that the proposed corrugation identification method achieves a higher precision rate and recall rate than those of traditional methods, reaching 99.67% and 98.34%, respectively. Besides, the validity and feasibility of the proposed methods for the rail corrugation period estimation and severity assessment are also investigated.

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Section: Rail Head Surface Localizationmentioning

confidence: 99%

Section: Performance Evaluation For Corrugation Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Identification and Assessment of Rail Corrugation Based on Computer Vision

Wei

Liu

et al. 2019

Applied Sciences

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“…But, the method is sensitive to the setting of algorithm parameters. In [21] and [22], rail corrugation identification methods based on rail image features in the frequency domain are studied. The local frequency features used in this method can reduce the detection time effectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model

et al. 2020

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The condition monitoring of railway track line is one of the essential tasks to ensure the safety of the railway transportation system. Railway track line is mainly composed of tracks, fasteners, sleepers, and so on. Given the requirements for rapid and accurate inspection, innovative and intelligent methods for multi-target defect identification of the railway track line using image processing and deep learning methods are proposed in this paper. Firstly, the track and fastener positioning method based on variance projection and wavelet transform is introduced. After that, a bag-of-visual-word (BOVW) model combined with spatial pyramid decomposition is proposed for railway track line multi-target defect detection with a detection accuracy of 96.26%. Secondly, an improved YOLOv3 model named TLMDDNet (Track Line Multi-target Defect Detection Network), integrating scale reduction and feature concatenation, is proposed to enhance detection accuracy and efficiency. Finally, to reduce model complexity and further improve the detection speed, with the help of dense connection structure, a lightweight design strategy for the TLMDDNet model named DC-TLMDDNet (Dense Connection Based TLMDDNet) is proposed, in which the DenseNet is applied to optimize feature extraction layers in the backbone network of TLMDDNet. The effectiveness of the proposed methods is demonstrated by the experimental results. INDEX TERMS Railway track line defects, multi-target defect identification, image processing, deep learning, YOLOv3.

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“…SVR employs the adaptive margin-based loss functions and delivers the learning data (non-linear or linear) to higher dimensional feature space. This method explores the best possible decision rule with regards to generalisation ability [15,16]. Regression estimation can be established by finding a function of y=f(x) based on a set of training sample { }, where is the input vector (training data set), is the objective value of training sample and N represents the number of training samples.…”

mentioning

confidence: 99%

Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation

et al. 2019

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Gradual deviation in track gauge of tram systems resulted from tram traffic is unavoidable. Tram gauge deviation is considered as an important parameter in poor ride quality and the risk of train derailment. In order to decrease the potential problems associated with excessive gauge deviation, implementation of preventive maintenance activities is inevitable. Preventive maintenance operation is a key factor in development of sustainable rail transport infrastructure. Track degradation prediction modelling is the basic prerequisite for developing efficient preventive maintenance strategies of a tram system. In this study, the data sets of Melbourne tram network is used and straight rail tracks sections are examined. Two model types including plain Support Vector Machine (SVM) and SVM optimised by Genetic Algorithm (GA- SVM) have been applied to the case study data. Two assessment indexes including Mean Squared Error (MSE) and the coefficient of determination (R2) are employed to evaluate the performance of the proposed models. Based on the results, GA-SVM model produces more accurate outcomes than plain SVM model.

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A cyber-enabled visual inspection system for rail corrugation

Cited by 38 publications

References 23 publications

The Identification and Assessment of Rail Corrugation Based on Computer Vision

The Identification and Assessment of Rail Corrugation Based on Computer Vision

Multi-Target Defect Identification for Railway Track Line Based on Image Processing and Improved YOLOv3 Model

Integration of Genetic Algorithm and Support Vector Machine to Predict Rail Track Degradation

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