• DocumentCode
    2042861
  • Title

    Subway lining segment faulting detection based on Kinect sensor

  • Author

    Xinwen Gao ; Liqing Yu ; Zhengzhe Yang

  • Author_Institution
    Sch. of Mechatron. Eng. & Autom., Shanghai Univ., Shanghai, China
  • fYear
    2015
  • fDate
    2-5 Aug. 2015
  • Firstpage
    1076
  • Lastpage
    1081
  • Abstract
    As a kind of tunnel diseases, faulting seriously affect the safety of the tunnel. It is essential to detect lining segment faulting effectively. Compare to traditional detection equipment with high price such as laser camera, a new measurement method for faulting detection by Kinect sensor with low price is proposed. After preprocessing the depth image data, which can be obtained the height difference image by double diagonal difference algorithm. Since the height difference image contains a lot of noise, this paper presents a method of combining the improved median filtering and connectivity domain filtering to solve it. Then recognise faulting, grouting hole and bolt hole through the shape feature. To extract faulting and thinning it. Finally, this paper puts forward a new algorithm called global search, which can identify and calculate the different forms of faulting. The experimental results show that the algorithm can detect and automatically identifies the subway lining segment faulting.
  • Keywords
    fault diagnosis; image denoising; median filters; railway safety; railways; Kinect sensor; bolt hole; connectivity domain filtering; depth image data; double diagonal difference algorithm; global search algorithm; grouting hole; height difference image; median filtering; subway lining segment faulting detection; tunnel safety; Fasteners; Feature extraction; Filtering; Image segmentation; Noise; Standards; faulting; height difference image; kinect; shape feature;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Automation (ICMA), 2015 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-7097-1
  • Type

    conf

  • DOI
    10.1109/ICMA.2015.7237635
  • Filename
    7237635