• Journal of Applied Optics
  • Vol. 44, Issue 1, 86 (2023)
Yi WANG1,2, Xiaojie GONG1,*, and Hao SU1,3
Author Affiliations
  • 1College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
  • 2Tangshan Technology Innovation Center of Intellectualization of Metal Component Production Line, Tangshan 063210, China
  • 3Tangshan Key Laboratory of Semiconductor Integrated Circuits, Tangshan 063210, China
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    DOI: 10.5768/JAO202344.0102004 Cite this Article
    Yi WANG, Xiaojie GONG, Hao SU. Image segmentation method of surface defects for metal workpieces based on improved U-net[J]. Journal of Applied Optics, 2023, 44(1): 86 Copy Citation Text show less
    Structure diagram of U-net network
    Fig. 1. Structure diagram of U-net network
    Structure diagram of CBAM network
    Fig. 2. Structure diagram of CBAM network
    Structure diagram of improved U-net network
    Fig. 3. Structure diagram of improved U-net network
    Visual platform for image acquisition
    Fig. 4. Visual platform for image acquisition
    Comparison before and after image preprocessing
    Fig. 5. Comparison before and after image preprocessing
    Variation curves of loss values
    Fig. 6. Variation curves of loss values
    Variation curves of mean intersection over union
    Fig. 7. Variation curves of mean intersection over union
    Variation curves of accuracy rate
    Fig. 8. Variation curves of accuracy rate
    Comparison of segmentation results
    Fig. 9. Comparison of segmentation results
    No.ApproachDice
    1PW-U-net0.8463
    2U-net0.8489
    3DO-U-net0.8516
    4Att-U-net0.8512
    5Att-DO-U-net0.8674
    Table 1. Comparison of Dice indexes
    Yi WANG, Xiaojie GONG, Hao SU. Image segmentation method of surface defects for metal workpieces based on improved U-net[J]. Journal of Applied Optics, 2023, 44(1): 86
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