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基于图像式传感器的铁路轨距检测系统研究

闵永智, 王红霞, 康飞, 党建武

闵永智, 王红霞, 康飞, 党建武. 基于图像式传感器的铁路轨距检测系统研究[J]. 激光技术, 2015, 39(3): 344-348. DOI: 10.7510/jgjs.issn.1001-3806.2015.03.014
引用本文: 闵永智, 王红霞, 康飞, 党建武. 基于图像式传感器的铁路轨距检测系统研究[J]. 激光技术, 2015, 39(3): 344-348. DOI: 10.7510/jgjs.issn.1001-3806.2015.03.014
MIN Yongzhi, WANG Hongxia, KANG Fei, DANG Jianwu. Study on rail gauge detection systems based on image sensors[J]. LASER TECHNOLOGY, 2015, 39(3): 344-348. DOI: 10.7510/jgjs.issn.1001-3806.2015.03.014
Citation: MIN Yongzhi, WANG Hongxia, KANG Fei, DANG Jianwu. Study on rail gauge detection systems based on image sensors[J]. LASER TECHNOLOGY, 2015, 39(3): 344-348. DOI: 10.7510/jgjs.issn.1001-3806.2015.03.014

基于图像式传感器的铁路轨距检测系统研究

基金项目: 

国家自然科学基金资助项目(60962004);铁道部科技计划资助项目(2010G014-G);甘肃省科技支撑计划资助项目(1104GKCA057);甘肃省自然科学研究基金资助项目(1308RJZA172);中国铁路总公司科技研究开发计划资助项目(2014X008-F)

详细信息
    作者简介:

    闵永智(1975-),男,博士研究生,副教授,主要从事智能测试及机器视觉方面的研究。

    通讯作者:

    党建武。E-mail:dangjw@mail.lzjtu.cn

  • 中图分类号: U216.3;TP212.9

Study on rail gauge detection systems based on image sensors

  • 摘要: 传感器在铁路安全维护方面应用广泛。为了实现实时在线轨距测量,将激光技术与CCD图像式传感器相结合,建立车载轨距机器视觉检测系统。首先介绍系统的构成与工作原理,并提出基于去噪前置与距离变换算法的快速轨道轮廓中心线提取方法。然后采用强对比度拉伸和指数变换的方法进行图像增强,并结合高斯平滑与动态感兴趣区域对图像进行快速去噪前置处理。最后对图像进行精确阈值分割处理,采用距离变换的方法得到轨道轮廓中心线并定位轨距测量点。结果表明,该系统检测精度满足-1mm~+1mm,图像帧处理速率为14.35m/s。轨距机器视觉检测系统满足实时在线轨距检测对系统鲁棒性、检测速度和精度的要求。
    Abstract: Sensors are widely used in railway safety maintenance. In order to achieve real-time online gauge measurement, an on-board machine vision system of gauge detection was established with the combination of laser technique and CCD image sensors. Firstly, the working principle and structure of the system were introduced. A fast extraction method of rail contour centerline was proposed based on pre-noising and distance transform (PNDT) algorithm. Then, the image was enhanced with the strong contrast stretching method and exponential transform method. Fast pre-noising processing was conducted with the combination of Gaussian smoothing and dynamic region of interest (ROI). Finally, precise threshold segmentation processing was carried out for the images. The rail contour centerline was gotten and the gauge measurement point was located with the distance transform method. Experimental results show that the detection accuracy of the system is -1mm~+1mm and image frame processing speed is 14.35ms. The system can meet the requirement of the robustness, detection speed and accuracy in real-time online gauge detection.
  • [1]

    SHOLL H, AMMAR R, GREENSHIELDS I, et al. Application of computing analysis to real-time railroad track inspection[C]//Automation Congress. New York, USA:IEEE,2006:1-6.

    [2]

    HUANG J X. The application study of image processing in track detection[D]. Beijing: Beijing Jiaotong University, 2007:33-83(in Chinese).

    [3]

    LIU T, REN S W, XU G Y, et al. Upgrading of gauge-alignment system of type GJ-4 track inspection car[J]. China Railway Science, 2006, 32(6):137-140(in Chinese).

    [4]

    RESENDIZ E, HART J M, AHUJA N. Automated visual inspection of railroad track[J].Intelligent Transportation Systems, 2013, 14(2):751-760.

    [5]

    RIZZO P, CAMMARATA M, BARTOLI I. Ultrasonic guided waves-based monitoring of rail head laboratory and field tests[J]. Advances in Civil Engineering, 2010, 6(10):1-13.

    [6]

    HU K, ZHOU F Q, ZHANG G J. An rapid sub-pixel accuracy extraction method for structured light stripe center[J]. Chinese Journal of Science Instrument, 2006, 27(10):1326-1329(in Chinese).

    [7]

    LEI H J, LI D H, WANG J Y, et al. An rapid detection method structured light stripe center[J]. Huazhong University of Science and Technology (Natural Science Edition), 2003, 31(1):74-76(in Chinese).

    [8]

    LI Z W, WANG C J, SHI Y S. An extraction algorithm for combination of gradient sharpening and optical center of gravity method[J]. Chinese Journal of Image and Graphics, 2008, 13(1): 64-68(in Chinese).

    [9]

    LI M, FENG H J, XU Z H, et al. A corrected image contour extraction method using intensity information for structured light[J]. Optical Engineering, 2005, 32(2):30-32(in Chinese).

    [10]

    ZHAN L, YU L, XIAO J, et al. Calibration method research of laser camera sensor in track detection[J]. Journal of Mechanical Engineering, 2013, 49(16):39-47(in Chinese).

    [11]

    MI C Z, XIE Z J, CHEN T, et al. The key technology of image enhancement and edge extraction of heavy rail[J]. Optics and Precision Engineering, 2012, 20(7):1645-1652(in Chinese).

    [12]

    OUYANG Ch S, YUAN J, TIAN J W, et al. An enhancement method for X-ray image using rough sets and human visual system [J]. Journal of Xi'an Jiaotong University, 2009, 43(6): 48-51(in Chinese).

    [13]

    SONKA M, HLAVAC V, BOYLE R. Image processing analysis and machine vision[M].3rd ed. Beijing: Tsinghua University Press, 2007:1-613(in Chinese).

    [14]

    ZHAO B H, WANG B X, ZHANG J, et al. Center extraction method for rough metal surface light strip[J]. Optics and Precision Engineering, 2011, 19(9):2138-2145(in Chinese).

    [15]

    ZHAO J, ZHAO J, ZHANG L. Image processing and feature extraction for structured light images of welded seam [J]. Journal of Xi'an Jiaotong University, 2013, 47(1):114-119(in Chinese).

    [16]

    WANG M J, YANG L, WANG X, et al.The class euclidean distance transformation class features extraction with a simplified pulse coupled neural network traffic sign image[J]. Optics and Precision Engineering, 2012, 20(12):2751-2758(in Chinese).

    [17]

    SAPIRO G. Geometric partial differential equations and image analysis[M].3rd ed. London,UK: Cambridge University Press, 2006:1-532.

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  • 被引次数: 21
出版历程
  • 收稿日期:  2014-04-20
  • 修回日期:  2014-05-19
  • 发布日期:  2015-05-24

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