Journal of Applied Optics, Volume. 44, Issue 5, 1022(2023)

Czochralski monocrystalline-silicon dislocation detection method based on improved YOLOv5 algorithm

Zhou YANG1, Ying CHENG1, Shijing ZHANG1, Xinyu TAO1, Xutao MO1, Sihai MA2, and Xianshan HUANG1、*
Author Affiliations
  • 1School of Science and Engineering of Mathematics and Physics, Anhui University of Technology, Ma'anshan 243002, China
  • 2Anhui Yixin Semiconductor Co.,Ltd., Hefei 231100, China
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    Figures & Tables(12)
    Frame diagram of improved YOLOv5s
    Schematic diagram of attention mechanism
    Frame diagram of channel attention mechanism
    Frame diagram of spatial attention mechanism
    Strengthening of feature fusion
    Dislocation corrosion pits of monocrystalline silicon
    Diagram of model training loss
    Comparison of different network performances
    Effect drawings of acid corrosion pits detected by different models
    Effect drawings of alkaline corrosion pits detected by different models
    • Table 1. Experimental environment setting

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      Table 1. Experimental environment setting

      ParameterConfiguration
      SystemLinux
      Deep learning frameworkPytorch
      Programming languagePython3.7
      CUDACUDA11.5
      GPURTX3080Ti
      CPUIntel(R) Xeon(R) Silver 4210R CPU @ 2.40 GHz
    • Table 2. Test results of different models

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      Table 2. Test results of different models

      MethodParameter /MBmAP@0.5FPS
      Faster-RCNN522.9149.01%13
      YOLOv3236.3293.26%43
      YOLOv526.9695.12%66
      Improved YOLOv536.2096.17%47
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    Zhou YANG, Ying CHENG, Shijing ZHANG, Xinyu TAO, Xutao MO, Sihai MA, Xianshan HUANG. Czochralski monocrystalline-silicon dislocation detection method based on improved YOLOv5 algorithm[J]. Journal of Applied Optics, 2023, 44(5): 1022

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    Paper Information

    Category: Research Articles

    Received: Sep. 30, 2022

    Accepted: --

    Published Online: Mar. 12, 2024

    The Author Email: Xianshan HUANG (黄仙山)

    DOI:10.5768/JAO202344.0502002

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