ViSyR: a Vision System for Real-Time Infrastructure Inspection

Marino, F.; Stella, Ettore

doi:10.5772/4985

Cited by 7 publications

(8 citation statements)

References 26 publications

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“…This result confirms our analysis that ME is mainly determined by the object entropy E O . 2) PEME costs less computation compared with ME since it need not compute the background entropy E B (t) in (13).…”

Section: Definition 2: Defect Proportion Refers To the Ratio Between mentioning

confidence: 99%

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A Visual Detection System for Rail Surface Defects

Ren

2012

IEEE Trans. Syst., Man, Cybern. C

123

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Discrete surface defects are the most common anomalies of rails and they should be carefully inspected. However, it is a challenge to detect such defects in a vision system because of illumination inequality and the variation of reflection property of rail surfaces. This paper presents an intelligent vision detection system (VDS) for discrete surface defects and focuses on two key issues of VDS: image enhancement and automatic thresholding. We propose the local Michelson-like contrast (MLC) measure to enhance rail images. MLC-based method is nonlinear and illumination independent; therefore, it notably improves the distinction between defects and background. In addition, we put forward the new automatic thresholding method-proportion emphasized maximum entropy (PEME) thresholding algorithm. PEME selects a threshold that maximizes the object entropy and meanwhile keeps the defect proportion in a low level. Our experimental results demonstrate that VDS detects the Type-II defects with a recall of 91.61% and Type-I defects with a recall of 88.53%, and the proposed MLC-based image enhancement method and PEME thresholding algorithm outperform the related well-established approaches.Index Terms-Automatic thresholding, contrast measure, image enhancement, maximum entropy (ME), rail surface defects.

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Section: Definition 2: Defect Proportion Refers To the Ratio Between mentioning

confidence: 99%

“…Corrugations are such defects that appear on the surface of a rail head in a repeatable or periodic manner [7], [13]. Discrete defects appear on the surface of a rail head in a random or arbitrary manner, i.e., with no characteristics of repeatability [14].…”

Section: Introductionmentioning

confidence: 99%

A Visual Detection System for Rail Surface Defects

Ren

2012

IEEE Trans. Syst., Man, Cybern. C

123

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“…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%

“…The Gabor texture features are first extracted from the entire rail image and then the K-nearest neighbor method is used for corrugation recognition, in which a high recognition precision is obtained. 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].…”

Section: Introductionmentioning

confidence: 99%

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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“…Computer vision techniques have been applied to extract some objects of the railway scene [2,3]. Some researches have focused on recognizing the bolts in the rails, and they face clear limitations to generate accurate measurements of rails and other objects.…”

Section: Introductionmentioning

confidence: 99%

Rail Track Irregularity Detection Method Based on Computer Vision and Gesture Analysis

Rong

Song²,

Dang³

et al. 2016

Int. J. Onl. Eng.

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Abstract-In this paper, rail track irregularity detection system based on computer vision and SVD analysis is proposed and located in the train's operator cabin near the front. Images are captured by FLEA3 camera of PointGrey, and vibration signals are collected by sensor device MPU6050 integrating 3-axis accelerometer and 3-axis gyroscope. Root mean square of gray-scale threshold Pulse Coupled Neural Network (RMS-PCNN) is used for segmentation of the rail track's image in a single loop, and the improved coupled map lattice(CML) is used for filtering the image and signifying the rail track. After perspective, the track radius can be fetched by analysis of regression. Vibration signal filtered by SVD-unscented Kalman filter(UKF) can reflect the wagon movements. In unscented Kalman filter, Cholesky is replaced by SDV in UT(unscented transform), which can solve negative definite matrix caused by covariance matrix on account of calculation error and round-off error. Also numerical stability is improved under the guarantee of filtering accuracy and the same complexity level of algorithm based on SVD-UKF. Looking up the radius record table, the corresponding threshold in gyroscope signal can be selected, and Compared to the super elevation, the invisible irregularity defects of rail bed will be found out.

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ViSyR: a Vision System for Real-Time Infrastructure Inspection

Cited by 7 publications

References 26 publications

A Visual Detection System for Rail Surface Defects

A Visual Detection System for Rail Surface Defects

The Identification and Assessment of Rail Corrugation Based on Computer Vision

Rail Track Irregularity Detection Method Based on Computer Vision and Gesture Analysis

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