Laser & Optoelectronics Progress, Volume. 60, Issue 15, 1524001(2023)

Metal Workpiece Surface Defect Segmentation Method Based on Improved U-Net

Yi Wang, Xiaojie Gong*, and Jia Cheng
Author Affiliations
  • College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China
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    Figures & Tables(9)
    U-net network structure
    Improved U-net network structure
    MAFE structure
    Bottleneck attention module
    Image acquisition visual platform
    Schematic diagram of cutting and filling. (a) Defect image; (b) image after processing
    Change curves of loss value
    Comparison of the segmentation effects. (a) Original images; (b) label; (c) U-net segmentation effect; (d) U-net-MAFE segmentation effect; (e) U-net-BAM segmentation effect; (f) U-net-MAFE-BAM segmentation effect
    • Table 1. Data comparison in the experimental network

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      Table 1. Data comparison in the experimental network

      NumberApproachmPAmIOUTest time /s
      1U-net0.84570.82530.156
      2U-net-MAFE0.85370.83650.158
      3U-net-BAM0.85830.84360.161
      4U-net-MAFE-BAM0.87490.86250.165
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    Yi Wang, Xiaojie Gong, Jia Cheng. Metal Workpiece Surface Defect Segmentation Method Based on Improved U-Net[J]. Laser & Optoelectronics Progress, 2023, 60(15): 1524001

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    Paper Information

    Category: Optics at Surfaces

    Received: Jun. 2, 2022

    Accepted: Jul. 26, 2022

    Published Online: Aug. 11, 2023

    The Author Email: Xiaojie Gong (1692994031@qq.com)

    DOI:10.3788/LOP221756

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