Laser & Optoelectronics Progress, Volume. 56, Issue 16, 161008(2019)

Surface Crack Detection Algorithm for Nuclear Fuel Pellets

Wenhao Song, Bin Zhang*, Fengyu Li, Tengda Yang, Jianning Li, and Xiaohui Yang
Author Affiliations
  • College of Physical Engineering, Zhengzhou University, Zhengzhou, Henan 450001, China
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    Figures & Tables(10)
    Flow chart of proposed method
    Pretreatment process. (a) Original image; (b) mask region; (c) region of interest
    Window scanning trace
    Structure of CrackCNN
    Loss and accuracy of CrackCNN
    Beamlets at different scales. (a) Scale is 0; (b) scale is 1; (c) scale is 2; (d) scale is 3
    Comparison of different threshold methods
    Detection results of three methods
    • Table 1. Parameter configurations of CrackCNN

      View table

      Table 1. Parameter configurations of CrackCNN

      LayerKernel shapeOutput channelStrideVariable
      Conv13×3×1321320
      Pool13×3×1-20
      Conv23×3×3248113872
      Pool23×3×1-20
      Conv35×5×4864176864
      Pool33×3×1-20
      Conv43×3×6480146160
      Pool43×3×1-20
      Fc18×8×80100-512100
      Fc21002-202
    • Table 2. Comparison of different methods

      View table

      Table 2. Comparison of different methods

      MethodPrecisionRecallF-measureTime /s
      Beamlet0.7010.7830.7413.6
      Doublethresholdand tensorvoting0.7400.8030.776.93
      Proposedmethod0.7750.8360.8042.41
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    Wenhao Song, Bin Zhang, Fengyu Li, Tengda Yang, Jianning Li, Xiaohui Yang. Surface Crack Detection Algorithm for Nuclear Fuel Pellets[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161008

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    Paper Information

    Category: Image Processing

    Received: Feb. 26, 2019

    Accepted: Mar. 22, 2019

    Published Online: Aug. 5, 2019

    The Author Email: Bin Zhang (zb1967@zzu.edu.cn)

    DOI:10.3788/LOP56.161008

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