A Visual Inspection System for Rail Corrugation Based on Local Frequency Features

Corrugation detection based on computer vision is a rapid and economical inspection technology, and it has attracted more and more attention from railway industry. This paper presents an effective corrugation detection system that includes an on-board image acquisition subsystem and a corrugation identification subsystem. In the corrugation identification subsystem, a track image captured by the on-board image acquisition subsystem is first segmented by the rail localization algorithm based on weighted projection profile. And then each column of the segmented rail image is represented by local frequency features and identified as corrugation line or not by a support vector machine (SVM). Lastly, the rail image is judged as corrugation by integrating the recognized corrugation lines. The experiment results show the precision and recall of the proposed corrugation detection system are 98.47% and 96.50%, respectively. They are 25% and 1% higher than those of traditional method. At the same time, the detection speed is doubly faster than that of the traditional method.

[1]  Ettore Stella,et al.  A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal-Headed Bolts Detection , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  K. H. Oostermeijer,et al.  Review on short pitch rail corrugation studies , 2008 .

[3]  Robin Clark,et al.  Rail flaw detection: overview and needs for future developments , 2004 .

[4]  Siwei Luo,et al.  A fast template matching-based algorithm for railway bolts detection , 2014, Int. J. Mach. Learn. Cybern..

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

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

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

[8]  Gemma Nicholson,et al.  Modelling of the response of an ACFM sensor to rail and rail wheel RCF cracks , 2012 .

[9]  M Ph Papaelias,et al.  A review on non-destructive evaluation of rails: State-of-the-art and future development , 2008 .

[10]  Narendra Ahuja,et al.  Automated Visual Inspection of Railroad Tracks , 2013, IEEE Transactions on Intelligent Transportation Systems.

[11]  Elena Kabo,et al.  Wheel/rail rolling contact fatigue – Probe, predict, prevent , 2014 .

[12]  Gui Yun Tian,et al.  Investigation into eddy current pulsed thermography for rolling contact fatigue detection and characterization , 2015 .

[13]  Fang Li,et al.  Detection of rail corrugation based on fiber laser accelerometers , 2013 .

[14]  Fei Xie,et al.  Freight train gauge-exceeding detection based on three-dimensional stereo vision measurement , 2012, Machine Vision and Applications.

[15]  Nicola Ancona,et al.  Filter-based feature selection for rail defect detection , 2004 .

[16]  Weining Liu,et al.  Study on the cause and treatment of rail corrugation for Beijing metro , 2014 .

[17]  William E. Higgins,et al.  Efficient Gabor filter design for texture segmentation , 1996, Pattern Recognit..

[18]  Jun Wang,et al.  A real-time computer vision-based platform for fabric inspection part 1: algorithm , 2015 .

[19]  Stuart L. Grassie,et al.  Rail corrugation: advances in measurement, understanding and treatment , 2005 .