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MIN Yong-zhi, YIN Chao, DANG Jian-wu, CHENG Tian-dong. Fast recognition method of rail region based on hue value mutation feature of image[J]. Journal of Traffic and Transportation Engineering, 2016, 16(1): 46-54. doi: 10.19818/j.cnki.1671-1637.2016.01.006
Citation: MIN Yong-zhi, YIN Chao, DANG Jian-wu, CHENG Tian-dong. Fast recognition method of rail region based on hue value mutation feature of image[J]. Journal of Traffic and Transportation Engineering, 2016, 16(1): 46-54. doi: 10.19818/j.cnki.1671-1637.2016.01.006
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Fast recognition method of rail region based on hue value mutation feature of image

doi: 10.19818/j.cnki.1671-1637.2016.01.006

  • School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

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  • Author Bio:

    MIN Yong-zhi (1975-), male, associate professor, PhD, +86-931-4938656, minyongzhi@mail.lzjtu.cn

  • Received Date: 2015-09-25
  • Publish Date: 2016-02-25
    Abstract
  • The rail boundary points in track inspection image were extracted by detecting the hue value mutation features of different regions in the HSL color space of color images.The rail's edges were determined by the linear fitting of multiple rail boundary points of different bisectors, and then the target rail region was recognized.The distribution features of sleeper, ballast, fastener and rail and the hue value mutation features of different characteristic regions were analyzed in the captured sequence images of track inspection system using machine vision.The correspondences between the hue value mutation point and the rail boundary points of different bisectors under different numbers of equal parts were researched.The influences of different numbers of equal parts on recognition time and recognition failure rate were discussed.The recognition method was compared with the traditional method under different light conditions.Analysis result indicates that when the number of equal parts is 8, the optimal recognition effect is obtained, and the recognition failure rate is 5.0%, the recognition time is 4.65 ms.In thethree intervals of characteristic light intensity, such as 500-1 000, 1 000-10 000, and 10 000-100 000 lx, the average maximum recognition times of recognition method in the rail regions of tracks with wood sleepers and concrete sleepers are 4.57 ms and 4.48 ms respectively, and are 44.4% and 47.1% less than the values of traditional method respectively.The standard deviations of recognition times are 0.15 ms and 0.12 ms respectively, and are 91.8% and 93.6% lower than the values of traditional method respectively.The average biggest recognition failure rates of proposed method are 3.5% and 3.3% respectively, and are 66.0% and 76.9% less than the values of traditional method respectively.The standard deviations of recognition failure rate are 1.6%, and are 68.9% and 71.1% lower than the values of traditional method respectively.So the proposed method is an effective recognition method of target rail region in the track inspection system using machine vision.

     

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    • References(25)
  • [1]
    LI Ying, OTTO C, HAAS N, et al. Component-based track inspection using machine-vision technology[C]∥ACM. Proceedings of the 1 st ACM International Conference on Multimedia Retrieval. New York: ACM, 2011: 1-8.
    [2]
    LI Yi-fan, LIU Jian-xin, LIN Jian-hui, et al. Fault diagnosis method of railway vehicle with wheel flat based on selfadaptive multi-scale morphology analysis[J]. Journal of Traffic and Transportation Engineering, 2015, 15(1): 58-65. (in Chinese). doi: 10.3969/j.issn.1671-1637.2015.01.008
    [3]
    DEUTSCHL E, GASSER C, NIEL A, et al. Defect detection on rail surfaces by a vision based system[C]∥IEEE. Proceeings of 2004IEEE Intelligent Vehicles Symposium. New York: IEEE, 2004: 507-511.
    [4]
    SINGH M, SINGH S, JAISWAL J, et al. Autonomous rail track inspection using vision based system[C]∥IEEE. Proceedings of the 2006IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety. New York: IEEE, 2006: 56-59.
    [5]
    ZHAN Dong, YU Long, XIAO Jian, et al. Study on track wear cross-section measurement utilizing laser-photogrammetric technique[J]. Journal of the China Railway Society, 2014, 36(4): 32-37. (in Chinese). doi: 10.3969/j.issn.1001-8360.2014.04.006
    [6]
    HUA Chun-jian, DENG Chao-sheng, CHEN Ying. Industrial products surface defects detection based on image subtraction with double masks[J]. Transducer and Microsystem Technologies, 2015, 34(5): 127-129, 133. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CGQJ201505037.htm
    [7]
    CHEN Xu-wen, LIU Gui-xiong, HUANG Jian. Method of image acquisition and curved surface eliminating for flexible printed circuit[J]. Journal of Electronic Measurement and Instrumentation, 2015, 29(6): 895-900. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201506020.htm
    [8]
    TRINH H, HAAS N, LI Ying, et al. Enhanced rail component detection and consolidation for rail track inspection[C]∥IEEE. IEEE Workshop on Applications of Computer Vision. New York: IEEE, 2012: 289-295.
    [9]
    BABENKO P. Visual inspection of railroad tracks[J]. Dissertationsand Theses-Gradworks, 2009, 3(4): 14-16. https://www.crcv.ucf.edu/papers/theses/Babenko_Pavel.pdf
    [10]
    LI Qing-yong, REN Sheng-wei. A visual detection system for rail surface defects[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012, 42(6): 1531-1542. doi: 10.1109/TSMCC.2012.2198814
    [11]
    LI Qing-yong, LIANG Zheng-ping, HUANG Ya-ping, et al. Sparseness representation model for defect detection and its application[J]. Journal of Computer Research and Development, 2014, 51(9): 1929-1935. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201409006.htm
    [12]
    GUO Bao-qing, ZHU Li-qiang, SHI Hong-mei, et al. Research on track inspection car position correction method by use of single image[J]. Journal of the China Railway Society, 2011, 33(12): 72-77. (in Chinese). doi: 10.3969/j.issn.1001-8360.2011.12.012
    [13]
    JESUSSEK M, ELLERMANN K. Fault detection and isolation for a railway vehicle by evaluating estimation residuals[J]. Procedia IUTAM, 2015, 13(1): 14-23. https://www.sciencedirect.com/science/article/pii/S221098381500005X
    [14]
    TANG Xiang-na, WANG Yao-nan. Visual inspection and classification algorithm of rail surface defect[J]. Computer Engineering, 2013, 39(3): 25-30. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC201303008.htm
    [15]
    LIU Xiao-rui, XIE Xiong-yao. Rapid crack inspection of tunnel surface based on image processing[J]. Chinese Journal of Underground Space and Engineering, 2009, 31(S2): 1624-1628. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BASE2009S2044.htm
    [16]
    WU Lu-shen, LI Yu-wen, CHEN Hua-wei, et al. Research on rail defects automatic detection technology based on image region partition[J]. Laser and Infrared, 2012, 42(5): 594-599. (in Chinese). doi: 10.3969/j.issn.1001-5078.2012.05.025
    [17]
    MAO Zheng-chong, WU Feng. Visual detection algorithm for rail surface defects based on image sensor[J]. Transducer and Microsystem Technologies, 2015, 34(9): 141-144. (in Chinese).
    [18]
    ZHOU Fu-qiang, GUO Hui. Fast segmentation method for brake beam of freight train under complex background[J]. Optical Technique, 2013, 39(5): 387-392. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-GXJS201305002.htm
    [19]
    EFTEKHARI M, MOALLEM M, SADRI S, et al. A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging[J]. Infrared Physics and Technology, 2013, 61(5): 330-336. https://www.sciencedirect.com/science/article/pii/S1350449513001837
    [20]
    LI Qing-yong, REN Sheng-wei. A real-time visual inspection system for discrete surface defects of rail heads[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(8): 2189-2199. https://www.docin.com/p-826978307.html
    [21]
    GIUSEPPE M G D, TROIANO A, TROISE C, et al. k-means clustering as tool for multivariate geophysical data analysis. An application to shallow fault zone imaging[J]. Journal of Applied Geophysics, 2013, 101(1): 108-115.
    [22]
    VASUKI Y, HOLDEN E J, KOVESI P, et al. Semi-automatic mapping of geological structures using UAV-based photogrammetric data: an image analysis approach[J]. Computers and Geosciences, 2014, 69(4): 22-32.
    [23]
    MO Si-te, LIU Tian-qi, LI Bi-xiong. Research of automatic white balance algorithm based on HSL color space[J]. Journal of Sichuan University: Engineering Science Edition, 2013, 45(S1): 95-99. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SCLH2013S1019.htm
    [24]
    LIANG Zhen, XU Bin-gang, CHI Zhe-ru, et al. Intelligent characterization and evaluation of yarn surface appearance using saliency map analysis, wavelet transform and fuzzy ARTMAP neural network[J]. Expert Systems with Applications, 2012, 39(4): 4201-4212. https://www.sciencedirect.com/science/article/pii/S0957417411014370
    [25]
    LIU Fen-fei, ZHAO Hui, TAO Wei. Improved edge detection method for the linear CCD image on linear fitting[J]. OptoElectronic Engineering, 2005, 32(3): 40-43. (in Chinese. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC20050300A.htm
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