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基于改进U-net的金属工件表面缺陷图像分割方法

王一 龚肖杰 苏皓

王一, 龚肖杰, 苏皓. 基于改进U-net的金属工件表面缺陷图像分割方法[J]. 应用光学, 2023, 44(1): 86-92. doi: 10.5768/JAO202344.0102004
引用本文: 王一, 龚肖杰, 苏皓. 基于改进U-net的金属工件表面缺陷图像分割方法[J]. 应用光学, 2023, 44(1): 86-92. doi: 10.5768/JAO202344.0102004
WANG Yi, GONG Xiaojie, SU Hao. Image segmentation method of surface defects for metal workpieces based on improved U-net[J]. Journal of Applied Optics, 2023, 44(1): 86-92. doi: 10.5768/JAO202344.0102004
Citation: WANG Yi, GONG Xiaojie, SU Hao. Image segmentation method of surface defects for metal workpieces based on improved U-net[J]. Journal of Applied Optics, 2023, 44(1): 86-92. doi: 10.5768/JAO202344.0102004

基于改进U-net的金属工件表面缺陷图像分割方法

doi: 10.5768/JAO202344.0102004
基金项目: 河北省高等学校科学技术研究项目(ZD2022114);唐山市科技计划项目(21130212C)
详细信息
    作者简介:

    王一(1981—),男,副教授,博士,主要从事视觉检测与感知技术方面的研究。E-mail:wangyi@ncst.edu.cn

    通讯作者:

    龚肖杰(1994—),女,硕士,主要从事机器视觉、目标识别方面的研究。E-mail:1692994031@qq.com

  • 中图分类号: TN209;TP391

Image segmentation method of surface defects for metal workpieces based on improved U-net

  • 摘要: 针对金属工件表面小尺寸缺陷及受非均匀光照影响的图像缺陷难以分割的问题,提出了一种改进的U-net语义分割网络,实现金属工件表面缺陷图像的精确分割。首先,在U-net网络中融入CBAM(convolutional block attention module)模块来提升图像中缺陷目标的显著度;其次,采用深度超参数化卷积DO-Conv(depthwise over-parameterized convolutional)代替网络中部分传统卷积,增加网络可学习的参数数量;然后,采用Leaky Relu函数代替网络中部分Relu函数,提高模型对负区间的特征提取能力;最后,采用中值滤波及非均匀光照的补偿方法进行图像预处理,减弱非均匀光照对金属工件图像表面缺陷的影响。结果表明:改进后的网络平均交并比、准确率和Dice系数指标分别达到0.8335、0.9332、0.8674,改进的网络显著提升了对金属工件表面缺陷图像的分割效果。
  • 图  1  U-net网络结构图

    Fig.  1  Structure diagram of U-net network

    图  2  CBAM结构示意图

    Fig.  2  Structure diagram of CBAM network

    图  3  改进的U-net网络结构图

    Fig.  3  Structure diagram of improved U-net network

    图  4  图像采集视觉平台

    Fig.  4  Visual platform for image acquisition

    图  5  图像预处理前后对比图

    Fig.  5  Comparison before and after image preprocessing

    图  6  损失值变化曲线图

    Fig.  6  Variation curves of loss values

    图  7  平均交并比图

    Fig.  7  Variation curves of mean intersection over union

    图  8  准确率

    Fig.  8  Variation curves of accuracy rate

    图  9  分割结果对比图

    Fig.  9  Comparison of segmentation results

    表  1  Dice指标对比

    Table  1  Comparison of Dice indexes

    No.ApproachDice
    1PW-U-net0.8463
    2U-net0.8489
    3DO-U-net0.8516
    4Att-U-net0.8512
    5Att-DO-U-net0.8674
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-24
  • 修回日期:  2022-09-06
  • 网络出版日期:  2022-11-29
  • 刊出日期:  2023-01-17

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