Computer Science ›› 2018, Vol. 45 ›› Issue (11A): 274-277.

• Pattem Recognition & Image Processing • Previous Articles     Next Articles

Research on Intelligent Detection Method of Steel Rail Abrasion

ZHANG Xiu-feng, WANG Juan, DING Qiang   

  1. College of Electromechanical Engineering,Dalian Nationalities University,Dalian,Liaoning 116600,China
  • Online:2019-02-26 Published:2019-02-26

Abstract: In order to meet the actual demand,a new detection method of steel rail abrasion based on line laser image processing was proposed after analyzing current methods and characteristics of steel rail abrasion detecting equipment at home and abroad.The bending degree of line laser image on the wear of rail was used to determine the width and depth of steel rail abrasion.The edge points and centre points could be found by using roof-type edge detection method,then straight lines can be fitted by using these points.The optimal features combination is selected by removing the redundant features with high correlation.Finally,the experiment results show that the method could extract features amount effectively,and obtain the width and depth of the steel rail abrasion accurately.The characteristics of algorithm inculde small amount,simple and high precision.It lays the foundation for the development of steel rail abrasion detection device.

Key words: Correlation coefficient, Edge detection, Image processing, Steel rail abrasion

CLC Number: 

  • TH74
[1]王德明,王桂宝,张广明,等.基于激光轮廓扫描仪的钢轨磨耗检测系统[J].仪表技术与传感器,2015(10):90-91.
[2]陈坤.便携式钢轨磨耗检测系统的研究[D].长沙:中南大学,2007:1-8.
[3]JIN W R,ZHAN X Q,JIANG B H.Non-contact Rail-Wear Inspecting System Based on Image Understanding [C]∥Procee-ding of the 2007 IEEE International Conference on Mechatronics And Automation.Harbin,2007:3854-3858.
[4]郑树彬,柴晓冬,安小雪,等.基于动态模板的钢轨磨耗测量方法研究[J].中国铁道科学,2013,23(2):7-11.
[5]占栋,于龙,邱存勇,等.钢轨轮廓测量中的车体振动补偿问题研究[J].仪器仪表学报,2013,34(7):1625-1632.
[6]张秀峰.小型钢轨损伤自动检测装置的开发[J].大连民族大学学报,2016,18(3):217-220.
[7]徐会杰,刘启宾,彭华,等.基于轮轨接触的高速铁路钢轨磨耗量[J].北京交通大学学报(自然科学版),2014,38(3):44-49.
[8]孟佳,高晓蓉.钢轨磨耗检测技术的现状与发展[J].铁道技术监督,2005(1):34-36.
[9]常治学,王培昌,逄凌滨,等.基于抛物线拟合的十字激光图像屋脊边缘检测[J].光电工程,2009,36(9):93-97.
[10]苑玮琦,荆澜涛,林森,等.基于分类区分度和相关性的手形特征选择方法[J].仪器仪表学报,2013,34(8):1787-1794.
[11]BOGDAN M,FITA S.Measurement of the Geometry of the Transverse Cross-section of a railway[J].Measurement Science Review,2003(3):747-751.
[12]王伟华,孙军华,刘震,等.钢轨磨耗动态测量结构光条纹中心提取算法[J].激光与红外,2010,40(1):87-90.
[13]JIN W R,ZHANG X Q,JIANG B H.Non-Contact Rail-wear Inspecting System Based on Image Understanding[C]∥Procee-ding of the 2007 IEEE,International Conference on Mechatronics and Automation.Harbin,2007:3854-3858.
[14]李立明,柴晓冬,郑树彬.基于图像处理的轨道轮廓提取算法[J].上海工程技术大学学报,2009,23(2):170-174.
[15]高晓蓉,王黎,赵全轲,等.线阵CCD传感器检测铁路轨道不平顺状态[J].光电工程,2002,29(3):50-53.
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