Laser & Optoelectronics Progress, Volume. 60, Issue 16, 1615007(2023)

Aero-Engine Surface Defect Detection Model Based on Improved YOLOv5

Xin Li, Xiangrong Li*, Cheng Wang, Qiuliang Li, and Zhuoyue Li
Author Affiliations
  • Fundamentals Department, Air Force Engineering University, Xi'an 710038, Shaanxi, China
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    Figures & Tables(12)
    YOLOv5s-6.0 network structure
    Flowchart of a search algorithm for data augmentation
    Structure of the CA module
    Diagram of CIoU
    Diagram of the CA module adding location
    Type of defect. (a) Crack; (b) gap; (c) pit; (d) scratch
    Defect labelling example
    xml label file
    Comparison of the detection effect of two models. (a) (b) (c) Detection effect of YOLOv5s; (d) (e) (f) Detection effect of YOLOv5-CE
    • Table 1. Search space

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      Table 1. Search space

      Data augmentation substrategyDescriptionRange of Vmagnitude
      ContrastAdjust the contrast of the image. Vmagnitude=0 gives a gray image and Vmagnitude=1 gives the original image[0,2]
      SharpnessAdjust the sharpness of the image. Vmagnitude=0 gives a blurred image and Vmagnitude=1 gives the original image[0,2]
      BrightnessAdjust the brightness of the image. Vmagnitude=0 gives a black image and Vmagnitude=1 gives the original image[0,2]
      RotationRotate the image by Vmagnitude degrees[-90°,90°]
      ScaleEnlarge or reduce the image to Vmagnitude scales[0.5,2]
      FlipFlip the imageFlip up-down/flip left-right
      HSV augmentationAdjust the H(hue),S(saturation),and V(value)of the imageH:[0°,360°],S:[0,1],V:[0,1]
      NoiseAdd noise to the imageGaussian noise and salt and pepper noise
    • Table 2. Comparison of the detection performance of algorithms

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      Table 2. Comparison of the detection performance of algorithms

      ModelPAP /%PmAP /%Speed /(frame/s)Capacity /MB
      crackgappitscratch

      Faster

      R-CNN

      75.478.158.783.173.814.29109
      YOLOv382.189.885.176.183.322.63235
      YOLOv490.093.286.679.087.224.72244
      YOLOv5s94.999.598.496.597.341.3214.4
      YOLOXs90.890.790.990.890.8110.6268.7
      YOLOv5-CE97.399.499.298.298.540.6514.5
    • Table 3. Ablation experiments

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      Table 3. Ablation experiments

      ModelRprecision /%Rrecall /%PAP /%PmAP /%Speed /(frame/s)
      crackgappitscratch
      YOLOv5s97.794.794.999.598.496.597.341.32
      YOLOv5_A96.895.795.299.598.997.897.940.49
      YOLOv5-C97.895.796.999.599.396.998.241.15
      YOLOv5-E97.396.696.499.599.398.298.446.73
      YOLOv5-CE98.195.497.399.499.298.298.540.65
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    Xin Li, Xiangrong Li, Cheng Wang, Qiuliang Li, Zhuoyue Li. Aero-Engine Surface Defect Detection Model Based on Improved YOLOv5[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1615007

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    Paper Information

    Category: Machine Vision

    Received: Sep. 15, 2022

    Accepted: Nov. 23, 2022

    Published Online: Aug. 18, 2023

    The Author Email: Li Xiangrong (lixiangrong0925@126.com)

    DOI:10.3788/LOP222557

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