基于2D的钢轨轮廓特征点提取方法研究
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摘要
钢轨轮廓数据特征点快速、准确提取是保证钢轨轮廓精确匹配、轨道几何不平顺精确检测的前提。对基于二维激光位移传感器(2D)的钢轨轮廓测量数据特征点提取方法进行研究,通过对采集的钢轨半断面轮廓数据采用基于中值误差与连续度自适应调整权值的平滑滤波方法对实测轮廓数据进行平滑处理,解决存在分段的轮廓数据达到分段平滑的效果。提出钢轨轮廓特征曲线的概念,并给出特征曲线的一种定义方式,利用特征曲线上的特征点去快速定位实测轮廓特征点。最后,采用GJ-2型轨道检测车进行试验,通过对实际轨道进行轮廓测量,采用本文所提出的特征点提取方法对实测轮廓数据进行特征点提取,试验证明,该方法能快速、准确地定位轮廓特征点。
Rapid and accurate extraction of feature points of rail contour data is theprerequisite to ensure accurate matching of rail contour and detection of track geometric irregularity. This paper conducts a study on the extraction method of the rail profile measurement data features based on the two-dimensional laser displacement sensor(2 D). By using smoothing filtering method based on mid-value error and continuous self-adaption adjusting value,the paper carries out the smoothing filtering of the measured profile data to allow the sectional profile data being sectionally smoothed. The concept of rail profile feature curve is put forward,a definition method of feature curve is given,and measured profile feature points are rapidly located based on feature points on the feature curve. Finally,GJ-2 track inspection car is employed to measure the actual track profile with the feature point extraction method proposed in this paper to extract the measured data feature points. The test results show that this method can locate feature points quickly and accurately.
Rapid and accurate extraction of feature points of rail contour data is theprerequisite to ensure accurate matching of rail contour and detection of track geometric irregularity. This paper conducts a study on the extraction method of the rail profile measurement data features based on the two-dimensional laser displacement sensor(2 D). By using smoothing filtering method based on mid-value error and continuous self-adaption adjusting value,the paper carries out the smoothing filtering of the measured profile data to allow the sectional profile data being sectionally smoothed. The concept of rail profile feature curve is put forward,a definition method of feature curve is given,and measured profile feature points are rapidly located based on feature points on the feature curve. Finally,GJ-2 track inspection car is employed to measure the actual track profile with the feature point extraction method proposed in this paper to extract the measured data feature points. The test results show that this method can locate feature points quickly and accurately.
引文
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[14]占栋,于龙,肖建,等.钢轨轮廓全断面检测中轨廓动态匹配方法研究[J].铁道学报,2015,37(5):71-77.
[2]鞠标,朱洪涛,徐鞠萍,等.基于轨检车振动问题的钢轨轮廓匹配方法研究[J].振动与冲击,2017,36(3):65-69.
[3]Yang Q,Lin J.Track gauge dynamic measurement based on 2D laser displacement sensor[C]∥Mech-anic Automation and Control Engineering(MACE),2011 Second International Conference on.IEEE,2011:5473-5476.
[4]安小雪.基于动态模板的钢轨磨耗测量方法研究[D].上海:上海工程技术大学,2013.
[5]Alippi C,Casagrande E,Scotti F,et al.Composite real-time image processing for railways track profile measurement[J].IEEE Transactions on instrumentation and measurement,2000,49(3):559-564.
[6]张文景,许晓鸣.一种基于曲率提取轮廓特征点的方法[J].上海交通大学学报,1999,33(5):592-595.
[7]Menaka R,Chellamuthu C,Karthik R.Efficient feature point detection in CT images using Discrete Curvelet Transform[J].Journal of Scientific&Industrial Research,2013,72(5):312-315.
[8]陈燕新,戚飞虎.一种新的提取轮廓特征点的方法[J].红外与毫米波学报,1998,17(3):171-176.
[9]陈东生,田新宇.中国高速铁路轨道检测技术发展[J].铁道建筑,2008(12):82-86.
[10]Ren S,Gu S,Xu G,et al.A new track inspection car based on a laser camera system[J].Chinese Optics Letters,2011,9(3):44-47.
[11]谢勤岚.图像降噪的自适应高斯平滑滤波器[J].计算机工程与应用,2009,45(16):182-184.
[12]江亮亮,杨付正,任光亮.用于网格去噪的自适应双边滤波器[J].华南理工大学学报(自然科学版),2015(11):54-60.
[13]Shen Y,Barner K E.Fuzzy vector median-based surface smoothing[J].IEEE Transactions on Visualization and Computer Graphics,2004,10(3):252-265.
[14]占栋,于龙,肖建,等.钢轨轮廓全断面检测中轨廓动态匹配方法研究[J].铁道学报,2015,37(5):71-77.