A Coarse-to-Fine Model for Rail Surface Defect Detection

Computer vision systems have attracted much attention in recent years for use in detecting surface defects on rails; however, accurate and efficient recognition of possible defects remains challenging due to the variations shown by defects and also noise. This paper proposes a coarse-to-fine model (CTFM) to identify defects at different scales. The model works on three scales from coarse to fine: subimage level, region level, and pixel level. At the subimage level, the background subtraction model exploits row consistency in the longitudinal direction, and strongly filters the defect-free range, leaving roughly identified subimages within which defects may exist. At the next level, the region extraction model, inspired by visual saliency models, locates definite defect regions using phase-only Fourier transforms. At the finest level, the pixel subtraction model uses pixel consistency to refine the shape of each defect. The proposed method is evaluated using Type-I and Type-II rail surface defect detection data sets and an actual rail line. The experimental results show that CTFM outperforms state-of-the-art methods according to both the pixel-level index and the defect-level index.

[1]  Qiaosong Wang,et al.  GraB: Visual Saliency via Novel Graph Model and Background Priors , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Zhaowei Shang,et al.  Self-organizing Map-Based Object Tracking with Saliency Map and K-Means Segmentation , 2014, CCPR.

[3]  Huchuan Lu,et al.  Visual saliency detection based on Bayesian model , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  Qingyong Li,et al.  A Hierarchical Extractor-Based Visual Rail Surface Inspection System , 2017, IEEE Sensors Journal.

[5]  R. Clark,et al.  Ultrasonic characterisation of defects in rails , 2002 .

[6]  F C Cruz,et al.  Efficient feature selection for neural network based detection of flaws in steel welded joints using ultrasound testing , 2017, Ultrasonics.

[7]  Dar-Shyang Lee,et al.  Effective Gaussian mixture learning for video background subtraction , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[9]  Rama Chellappa,et al.  Robust Fastener Detection for Autonomous Visual Railway Track Inspection , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[10]  Yasuhiro Sato,et al.  Formation mechanism and countermeasures of rail corrugation on curved track , 2002 .

[11]  Qingyong Li,et al.  A Real-Time Visual Inspection System for Discrete Surface Defects of Rail Heads , 2012, IEEE Transactions on Instrumentation and Measurement.

[12]  Liming Zhang,et al.  Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Qingyong Li,et al.  A Visual Detection System for Rail Surface Defects , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  C. Koch,et al.  A saliency-based search mechanism for overt and covert shifts of visual attention , 2000, Vision Research.

[15]  Vincenzo Piuri,et al.  Composite real-time image processing for railways track profile measurement , 2000, IEEE Trans. Instrum. Meas..

[16]  Yi Shen,et al.  Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy , 2015 .

[17]  Christof Koch,et al.  Image Signature: Highlighting Sparse Salient Regions , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Wai Lok Woo,et al.  Automatic Defect Identification of Eddy Current Pulsed Thermography Using Single Channel Blind Source Separation , 2014, IEEE Transactions on Instrumentation and Measurement.

[19]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Jianwu Dang,et al.  Real time detection system for rail surface defects based on machine vision , 2018, EURASIP Journal on Image and Video Processing.

[21]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

[22]  Nuno Vasconcelos,et al.  Decision-Theoretic Saliency: Computational Principles, Biological Plausibility, and Implications for Neurophysiology and Psychophysics , 2009, Neural Computation.

[23]  Shafeeq Ahmad,et al.  Crack Detection in Railway Track Using Image Processing , 2017 .

[24]  Yaonan Wang,et al.  Surface defect detection for high-speed rails using an inverse P-M diffusion model , 2016 .

[25]  Stuart L. Grassie,et al.  Rail corrugation: Characteristics, causes, and treatments , 1993 .

[26]  Francesco Corman,et al.  A train rescheduling model integrating speed management during disruptions of high-speed traffic under a quasi-moving block system , 2017 .

[27]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[28]  Thierry Bouwmans,et al.  Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey , 2011 .

[29]  Bu-Sung Lee,et al.  Bottom-Up Saliency Detection Model Based on Human Visual Sensitivity and Amplitude Spectrum , 2012, IEEE Transactions on Multimedia.

[30]  Bart De Schutter,et al.  Deep convolutional neural networks for detection of rail surface defects , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[31]  Shao-Yi Chien,et al.  Real-Time Salient Object Detection with a Minimum Spanning Tree , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Weisi Lin,et al.  Saliency-Based Defect Detection in Industrial Images by Using Phase Spectrum , 2014, IEEE Transactions on Industrial Informatics.

[33]  Long Chen,et al.  Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems , 2014, IEEE Transactions on Instrumentation and Measurement.

[34]  Pietro Perona,et al.  Graph-Based Visual Saliency , 2006, NIPS.

[35]  Eitan M. Gurari,et al.  Introduction to the theory of computation , 1989 .

[36]  David M. J. Tax,et al.  Semi-supervised rail defect detection from imbalanced image data , 2016 .

[37]  E. Stella,et al.  Visual recognition of fastening bolts for railroad maintenance , 2004, Pattern Recognit. Lett..

[38]  Klaus Knothe,et al.  Review on rail corrugation studies , 2002 .

[39]  Wonjun Kim,et al.  Saliency Combined Particle Filtering for Aircraft Tracking , 2014, J. Signal Process. Syst..

[40]  M. N. Bassim,et al.  Detection of the onset of fatigue crack growth in rail steels using acoustic emission , 1994 .

[41]  Laurent Itti,et al.  Visual salience , 2007, Scholarpedia.

[42]  Patrice Aknin,et al.  On-line rail defect diagnosis with differential eddy current probes and specific detection processing , 2003 .

[43]  Lianghai Jin,et al.  Characteristic analysis of Otsu threshold and its applications , 2011, Pattern Recognit. Lett..

[44]  Dimitris Samaras,et al.  Texture classification for rail surface condition evaluation , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[45]  Nanning Zheng,et al.  Visual Saliency Based Object Tracking , 2009, ACCV.