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Laser & Optoelectronics Progress, Volume. 60, Issue 4, 0415009(2023)

Steel-Plate Surface-Defect Detection Algorithm Based on Improved YOLOv5s

Yan Zhou1、*, Jiangnan Meng1、**, Jia Wu1,1、">, Zhi Luo2,2、">, and Dongli Wang1,1、">
Author Affiliations
  • 1School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, Hunan, China
  • 2Hunan Valin Xiangtan Iron and Steel Co., Ltd., Xiangtan 411105, Hunan, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (20)
    References (26)
    Cited By (1)
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    Figures & Tables(11)
    YOLOv5s network structure

    Fig. 1. YOLOv5s network structure

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    Mosaic data expansion with MixUp

    Fig. 2. Mosaic data expansion with MixUp

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    CBAM structure

    Fig. 3. CBAM structure

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    Fusion attention module

    Fig. 4. Fusion attention module

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    AP statistics of various types of defects

    Fig. 5. AP statistics of various types of defects

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    AP comparison of various types of defects before and after improvement of loss function

    Fig. 6. AP comparison of various types of defects before and after improvement of loss function

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    Data set and statistic of samples of each defect

    Fig. 7. Data set and statistic of samples of each defect

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    Comparison of detection results before and after improvement. (a) Original YOLOv5s; (b) improved YOLOv5s

    Fig. 8. Comparison of detection results before and after improvement. (a) Original YOLOv5s; (b) improved YOLOv5s

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    • Table 1. Anchor box statistics table

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      Table 1. Anchor box statistics table

      MethodAnchor boxFitness /%
      Raw（10，13）（16，30）（33，23）（30，61）（62，45）（59，119）（116，90）（156，198）（373，326）68.46
      Euclidean（31，42）（36，86）（80，63）（38，206）（64，136）（135，79）（199，60）（109，200）（201，207）72.87
      IoU（20，39）（25，82）（50，45）（62，82）（192，31）（34，190）（146，74）（78，166）（185，188）75.56
      Genetic（20，42）（25，82）（51，46）（176，29）（64，85）（34，190）（146，74）（78，166）（185，188）75.91

    • Table 2. Ablation experiments of YOLOv5s

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      Table 2. Ablation experiments of YOLOv5s

      MethodAnchorMixUpCBAM

      Focal

      loss

      		Precision /%Recall /%Number of parametersmAP /%
      YOLOv5s77.172.5703581175.4
      Improved 1✔75.973.8703581176.2
      Improved 2✔✔77.072.6703581176.9
      Improved 3✔✔✔77.972.3706867778.2
      Improved 4✔✔✔✔78.073.2706867778.4

    • Table 3. Performance comparison of different models

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      Table 3. Performance comparison of different models

      NetworkSpeed /（frame·s-1）mAP /%AP /%
      crazinginclusionpatchespitted_surfacerolled-in_scalescratches
      YOLOv32969.144.760.884.474.561.187.2
      DDN（ResNet34）［16］<2074.84875.987.478.368.490.8
      DDN（ResNet50）［16］<1082.362.484.790.389.776.390.1
      Faster R-CNN（ResNet34）［16］<2070.246.761.382.876.570.783.4
      YOLOv5s4175.437.684.292.183.761.893.1
      Improved YOLOv5s4078.448.883.891.680.572.792.8
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    Yan Zhou, Jiangnan Meng, Jia Wu, Zhi Luo, Dongli Wang. Steel-Plate Surface-Defect Detection Algorithm Based on Improved YOLOv5s[J]. Laser & Optoelectronics Progress, 2023, 60(4): 0415009

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    Paper Information

    Category: Machine Vision

    Received: Dec. 21, 2021

    Accepted: Mar. 29, 2022

    Published Online: Feb. 14, 2023

    The Author Email: Zhou Yan (yanzhou@xtu.edu.cn), Meng Jiangnan (mjnshizhu@163.com)

    DOI:10.3788/LOP213302

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology