ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Detection and recognition of high-speed railway catenary locator based on Deep Learning

  • 1.

    Institute of Automation, Chinese Academy of Sciences, Beijing, 100190

  • 2.

    University of Chinese Academy of Sciences, Beijing, 101408

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.04.006
  • Received Date: 28 August 2016
  • Rev Recd Date: 08 December 2016
  • Publish Date: 30 April 2017
    Abstract

  • Abstract

    High-speed rail monitoring is conducted mainly by adopting image processing and computer vision technology to detect, identify and track catenary components in image sequences taken by the visible light high-definition camera. In the entire monitoring system, the detection and recognition of the locator constitutes the very basis. It is difficult to design the feature descriptor with the characteristics of versatility, robustness and high-accuracy by using traditional target detection algorithms. #br##br#The detection of the high-accuracy locators based on the Faster R-CNN framework has been realized. Meanwhile, the Hough transform is used to detect the skeleton outline of the locator, and the optimal fitting straight line of the locator is extracted by the filtering mechanism, which paves the way for the non-contact precision measurement of the slope of the locators.

    Abstract

    High-speed rail monitoring is conducted mainly by adopting image processing and computer vision technology to detect, identify and track catenary components in image sequences taken by the visible light high-definition camera. In the entire monitoring system, the detection and recognition of the locator constitutes the very basis. It is difficult to design the feature descriptor with the characteristics of versatility, robustness and high-accuracy by using traditional target detection algorithms. #br##br#The detection of the high-accuracy locators based on the Faster R-CNN framework has been realized. Meanwhile, the Hough transform is used to detect the skeleton outline of the locator, and the optimal fitting straight line of the locator is extracted by the filtering mechanism, which paves the way for the non-contact precision measurement of the slope of the locators.
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    • References(23)
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    [1]
    张红, 王晓明, 曹洁,等. 基于大数据的智能交通体系架构[J]. 兰州理工大学学报, 2015, 41(2): 112-115.
    ZHANG Hong, WANG Xiaoming, CAO Jie, et al. Architecture of intelligent traffic systems based on big data [J]. Journal of Lanzhou University of Technology, 2015, 41(2): 112-115.
    [2]
    张娟, 毛晓波, 陈铁军. 运动目标跟踪算法研究综述[J]. 计算机应用研究, 2009, 26(12): 4407-4410.
    ZHANG Juan, MAO Xiaobo, CHEN Tiejun. Survey of moving object tracking algorithm[J].Computer Application Research, 2009, 26(12): 4407-4410.
    [3]
    LOWE D G. Distinctive image features from scale-invariant key points [J]. International Journal of Computer Vision, 2004, 60(2): 91-110.
    [4]
    BAY H, TUYTELAARS T, GOOL L V. SURF: Speeded up robust features [J]. Computer Vision & Image Understanding, 2006, 110(3) :404-417.
    [5]
    DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE Computer Society, 2005, 1: 886-893.
    [6]
    范虎伟,卞春华,朱挺,等.非接触式接触网定位器坡度自动检测技术[J].计算机应用, 2010, 30: 102-103.
    FAN H W, BIAN C H, ZHU T, et al. Automatic detection of positioning line in contactless overhead contact system[J]. Journal of Computer Applications, 2010, 30: 102-103.
    [7]
    顾会建. 基于视频图片的接触网定位器识别方法研究[D]. 成都: 西南交通大学, 2014.
    [8]
    李莹，叶培建，彭兢,等.火星探测出舱机构的识别定位与坡度测量[J]. 宇航学报, 2016, 37(2): 169-174.
    LI Ying, YE Peijian, PENG Jing, et al. Egress mechanism recognition and slope measurement for mars exploration [J]. Journal of Astronautics, 2016, 37(2): 169-174.
    [9]
    JADERBERG M, SIMONYAN K, VEDALDI A, et al. Reading text in the wild with convolutional neural networks[J]. International Journal of Computer Vision, 2014, 116(1):1-20.
    [10]
    GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE Computer Society, 2014: 580-587.
    [11]
    UIJLINGS J R R, VAN DE SANDE K E A, GEVERS T, et al. Selective search for object recognition [J]. International Journal of Computer Vision, 2013, 104(2): 154-171.
    [12]
    HE K M, ZHANG X, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]// Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland: ACM Press, 2014: 346-361.
    [13]
    GIRSHICK R. Fast R-CNN[C]// Proceedings of the IEEE International Conference on Computer Vision. Santiago, USA: IEEE Press, 2015: 1440-1448.
    [14]
    REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, arXiv: 1506.01497.
    [15]
    LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [16]
    HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.
    [17]
    HOUGH V, PAUL C. Method and means for recognizing complex patterns, 3069654, US1771560A [P]. 1962.
    [18]
    DUDA R O, HART P E. Use of the Hough transformation to detect lines and curves in pictures [J]. Communications of the ACM, 1972, 15(1): 11-15.
    [19]
    DING L J, GOSHTASBY A. On the canny edge detector[J]. Pattern Recognition, 2001, 34(3):721-725.
    [20]
    JIA Y Q, SHELHAMER E, DONAHUE J, et al. Caffe: Convolutional architecture for fast feature embedding[C]// Proceedings of the 22nd ACM International Conference on Multimedia. Orlando, USA: ACM Press, 2014: 675-678.
    [21]
    Caffe：清晰高效的深度学习(Deep Learning)框架[J/OL]. http://blog.csdn.net/ycheng_sjtu/article/details/39693655.
    [22]
    FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.
    [23]
    OSUNA E, FREUND R, GIROSI F. Training support vector machines: An application to face detection[J/OL]. http://doi.ieeecomputersociety.org/10.1109/CVPR.1997.609310, 2016.06.28.
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