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改进YOLOv4的表面缺陷检测算法

李彬 汪诚 丁相玉 巨海娟 郭振平 李卓越

李彬,汪诚,丁相玉,等. 改进YOLOv4的表面缺陷检测算法[J]. 北京航空航天大学学报,2023,49(3):710-717 doi: 10.13700/j.bh.1001-5965.2021.0301
引用本文: 李彬,汪诚,丁相玉,等. 改进YOLOv4的表面缺陷检测算法[J]. 北京航空航天大学学报,2023,49(3):710-717 doi: 10.13700/j.bh.1001-5965.2021.0301
LI B,WANG C,DING X Y,et al. Surface defect detection algorithm based on improved YOLOv4[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):710-717 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0301
Citation: LI B,WANG C,DING X Y,et al. Surface defect detection algorithm based on improved YOLOv4[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(3):710-717 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0301

改进YOLOv4的表面缺陷检测算法

doi: 10.13700/j.bh.1001-5965.2021.0301
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  • 中图分类号: V239;TP181

Surface defect detection algorithm based on improved YOLOv4

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  • 摘要:

    为解决航空发动机部件表面缺陷检测精度低、检测速度慢的问题,提出一种改进的YOLOv4算法进行智能检测。在路径聚合网络(PANet)结构中融合浅层特征与深层特征,增大特征检测尺度,同时去除自下而上的路径增强结构,提高小目标检测精度和整体检测速度;根据各类缺陷数量不同的情况,优化聚焦损失中的平衡参数,增加权重因子调节各类缺陷的损失权重,将改进后的聚焦损失代替分类误差中的交叉熵损失函数,降低样本不平衡和难易样本对检测精度的影响。实验表明:相比于原始YOLOv4算法,改进后的YOLOv4算法在测试集上的平均精度均值(mAP)为90.10%,提高了2.17%;检测速度为24.82 fps,提高了1.58 fps,检测精度也高于单发多框检测(SSD)算法、EfficientDet算法、YOLOv3算法和YOLOv4-Tiny算法。

     

  • 图 1  YOLOv4网络结构

    Figure 1.  YOLOv4 network structure

    图 2  改进的YOLOv4结构

    Figure 2.  Improved YOLOv4 structure

    图 3  缺陷类型

    Figure 3.  Defect types

    图 4  聚类中心为12的聚类结果

    Figure 4.  Clustering results when cluster center is 12

    图 5  不同模型的Fβ对比

    Figure 5.  Comparison of Fβ of different models

    图 6  各类缺陷样本的检测结果

    Figure 6.  Test results of various defect samples

    表  1  数据集中的目标数量

    Table  1.   Number of targets in dataset

    缺陷类型目标数量总和
    训练集验证集测试集
    裂纹1 5795805952 754
    缺口8482722701 390
    凹坑9313133491 593
    划痕1 7826286493 059
    下载: 导出CSV

    表  2  不同参数组合下的检测精度对比

    Table  2.   Comparison of detection accuracy under different parameter combinations

    α0β0mAP/%AP/%
    裂纹缺口凹坑划痕
    0.202.5088.0285.0486.9191.7388.38
    0.203.0088.2485.9487.7490.6688.60
    0.203.5089.0184.9090.6292.1888.32
    0.252.5089.0986.4090.9290.0788.95
    0.253.0090.1087.0991.2192.6589.45
    0.253.5088.4984.5590.0990.2789.06
    0.302.5089.0086.6287.6692.2989.41
    0.303.0088.6286.8690.6589.3087.68
    0.303.5088.3485.9889.5389.9187.94
    下载: 导出CSV

    表  3  不同模型的检测性能对比

    Table  3.   Comparison of detection performance of different models

    网络结构mAP/%Speed
    /fps
    AP/%
    裂纹缺口凹坑划痕
    YOLOv487.9323.2486.6492.2986.0286.78
    YOLOv4-A89.6124.9486.3693.1289.1089.86
    YOLOv4-B90.1024.8287.0991.2192.6589.45
     注:fps表示帧/s。
    下载: 导出CSV

    表  4  不同模型的网络结构参数量对比

    Table  4.   Comparison of network structure parameters of different models

    网络结构总参数量参数容量/MB
    YOLOv463 953 841243.96
    YOLOv4-A48 971 468186.81
    YOLOv4-B48 971 468186.81
    下载: 导出CSV

    表  5  不同算法的检测性能对比

    Table  5.   Comparison of detection performance of different algorithms

    网络结构mAP/%Speed/fps
    SSD60.0733.07
    EfficientDet-D049.3913.07
    EfficientDet-D157.7110.52
    EfficientDet-D263.409.45
    YOLOv386.2530.37
    YOLOv4-Tiny54.2564.55
    YOLOv487.9323.24
    YOLOv4-B90.1024.82
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-04
  • 录用日期:  2021-10-11
  • 网络出版日期:  2023-03-10
  • 整期出版日期:  2023-03-30

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