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数据与模型联合驱动的陶瓷材料晶粒分割

雷涛 李云彤 周文政 袁启斌 王成兵 张小红

雷涛, 李云彤, 周文政, 袁启斌, 王成兵, 张小红. 数据与模型联合驱动的陶瓷材料晶粒分割. 自动化学报, 2020, 46(x): 1−16 doi: 10.16383/j.aas.c200277
引用本文: 雷涛, 李云彤, 周文政, 袁启斌, 王成兵, 张小红. 数据与模型联合驱动的陶瓷材料晶粒分割. 自动化学报, 2020, 46(x): 1−16 doi: 10.16383/j.aas.c200277
Lei Tao, Li Yun-Tong, Zhou Wen-Zheng, Yuan Qi-Bin, Wang Cheng-Bing, Zahng Xiao-Hong. Grain segmentation of ceramic materials using data-driven jointing model-driven. Acta Automatica Sinica, 2020, 46(x): 1−16 doi: 10.16383/j.aas.c200277
Citation: Lei Tao, Li Yun-Tong, Zhou Wen-Zheng, Yuan Qi-Bin, Wang Cheng-Bing, Zahng Xiao-Hong. Grain segmentation of ceramic materials using data-driven jointing model-driven. Acta Automatica Sinica, 2020, 46(x): 1−16 doi: 10.16383/j.aas.c200277

数据与模型联合驱动的陶瓷材料晶粒分割

doi: 10.16383/j.aas.c200277
基金项目: 国家自然科学基金(61871259, 61811530325, 61976130)资助
详细信息
    作者简介:

    雷涛:陕西科技大学电子信息与人工智能学院教授, 2011年获得西北工业大学信息与通信工程专业博士学位, 主要研究方向为数字图像处理、模式识别与机器学习. 本文通信作者.Email: leitaoly@163.com

    李云彤:陕西科技大学电气与控制工程学院研究生, 2018年获得陕西科技大学自动化专业学士学位, 主要研究方向为数字图像处理.Email: yuntong_li@163.com

    周文政:陕西科技大学电气与控制工程学院研究生, 2017年获得重庆大学自动化专业学士学位, 主要研究方向为数字图像处理.Email: zhou_wenz@163.com

    袁启斌:陕西科技大学电子信息与人工智能学院副教授, 硕导, 2018年获西安交通大学电子科学与技术专业博士学位, 主要研究方向包括: 新型储能电介质材料与器件、柔性可穿戴材料与器件、材料微纳尺度结构解析等.Email: yuanqibin-sust@163.com

    王成兵:陕西科技大学材料科学与工程学院教授, 博导, 2008年获中国科学院兰州化学物理研究所物理化学专业博士学位, 主要研究方向为材料表面技术与涂层.Email: wangchengbing@gmail.com

    张小红:陕西科技大学文理学院教授, 2005年获得西北工业大学计算机软件与理论博士学位, 主要研究方向为模糊逻辑、粗糙集、不确定性数学、数据科学和人工智能.Email: hangxiaohong@sust.edu.cn

    通讯作者:

    陕西科技大学电子信息与人工智能学院教授, 2011年获得西北工业大学信息与通信工程专业博士学位, 主要研究方向为数字图像处理、模式识别与机器学习. 本文通信作者. Email: leitaoly@163.com

Grain Segmentation of Ceramic Materials Using Data-Driven Jointing Model-Driven

Funds: Supported by National Natural Science Foundation of P. R. China (61871259, 61811530325, 61976130)
More Information
    Corresponding author: LEI Tao Professor at the school of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology. He received his PhD degree in Information and Communication Engineering from Northwestern Polytechnical University in 2011. His research interest covers image processing and artificial intelligence. Corresponding author of this paper
  • 摘要: 研究陶瓷晶粒尺寸分布对估计陶瓷样品的物理属性具有重要意义, 当前主要依赖人工方法测量晶粒尺寸, 由于晶粒形状不规则且大小不一, 因此人工方法测量效率低、误差大. 针对该问题, 提出一种数据与模型联合驱动的陶瓷材料晶粒分割算法. 该算法首先通过图像预处理解决材料表面反光导致的灰度不均匀问题; 其次利用本文提出的鲁棒分水岭变换实现图像中晶粒的预分割, 解决传统分水岭算法存在的过分割以及分割区域个数与轮廓精度难以平衡的问题; 最后根据本文提出的轻量级富卷积特征网络输出晶粒轮廓并利用该轮廓对预分割结果进行优化. 与主流图像分割算法相比, 提出的算法一方面利用鲁棒分水岭变换实现了更为准确的晶粒区域定位, 另一方面利用图像的低层与高层特征融合获取了更为精准的晶粒轮廓. 实验结果表明, 提出的算法不仅能够实现陶瓷材料晶粒尺寸的精准计算, 而且具有较高的计算效率, 为分析陶瓷材料物理属性提供了客观准确的数据.
  • 图  1  总体流程图, 其中, SE为结构边缘算法(Structured Edge, SE)[32]

    Fig.  1  Overall flow chart, where SE denotes edge detection algorithm based on structured forests

    图  2  原图与预处理结果对比, 预处理算法能有效解决陶瓷SEM图像的灰度不均匀问题(a)-(d)为原图 (e)-(h)为经过预处理的图像

    Fig.  2  Comparison of original and pre-processed images, the pre-processing algorithm can effectively solve the issue of uneven grayscale of ceramic SEM image (a)-(d) original images (e)-(h) pre-processed images.

    图  3  不同参数的MGR-WT对图像的分割结果对比, $ r $是用于梯度重建的圆形结构元半径

    Fig.  3  Segmentation result comparison using MRG-WT with different parameters, $ r $ is the radius of structural element for gradient reconstruction

    图  4  RWT与MGR-WT对陶瓷材料晶粒的分割结果对比 (a) 未去除小区域的形态学分水岭算法分割结果(b) MGR-WT分割结果 (c) RWT分割结果 (d) RWT和MGR-WT的分割结果与Ground Truth对比(b线表示RWT结果, g线表示MGR-WT结果, p线表示Ground Truth结果)

    Fig.  4  Comparison of the segmentation results of ceramic grains between RWT and MGR-WT (a) segmentation result using watershed transform without removing small areas (b) segmentation result using MGR-WT (c) robust watershed segmentation (RWT) result (d) comparison of the segmentation results of RWT and MGR-WT with Ground Truth (line b for RWT, line g for MGR-WT and line p for Ground Truth)

    图  5  基于RWT的图像分割结果, 分割后结果存在双线轮廓问题

    Fig.  5  Segmentation results obtained by RWT suffer from the problem of double line contour

    图  6  去除双线 (a) RWT分割结果(b) 对(a)去除双线的结果 (c) 去除双线前后的结果对比

    Fig.  6  Removing double lines (a) segmentation result using RWT (b) removing double line from (a) (c) comparison of (a) and (b)

    图  7  深度可分离卷积

    Fig.  7  Depthwise separable convolution

    图  8  LRCF网络结构图

    Fig.  8  LRCF network structure

    图  9  基于LRCF与分水岭变换的图像分割 (a) 基于LRCF的轮廓预测 (b) 基于LRCF的MGR-WT结果 (c)基于LRCF的分割结果与Ground Truth对比(y线表示Ground Truth)

    Fig.  9  Image segmentation using the combination of LRCF and watershed transform (a) contour prediction using LRCF (b) segmentation result using the combination of LRCF and MGR-WT (c)comparison segmentation results of LRCF-MGR-WT and Ground Truth (line y for Ground Truth)

    图  10  轮廓优化 (a) 优化前结果 (b) 优化后结果 (c) 优化前后与Ground Truth对比(图中p线为优化结果, g线为优化前结果, y线为Ground Truth)

    Fig.  10  Contour optimization (a) the result before optimization (b) optimized results (c) comparison among (a), (b) and Ground Truth(line p for optimization result, line g for optimization result, line y for Ground Truth)

    表  1  不同方法对陶瓷晶粒分割的性能指标对比(第一组实验, 未镀金的图像)

    Table  1  Performance comparison of different approaches for ceramic grain segmentation (the first group of experiments for unplated image)

    MethodsCV↑VI↓GCE↓BDE↓
    Liu’s-MGR [38]0.28893.42700.47427.3230
    Random Walker [39]0.35562.90030.140713.2147
    SLIC [14]0.35473.05240.439610.1678
    LSC [15]0.34552.88200.35637.5911
    Banerjee’s [30]0.59592.19920.20313.9182
    SE-MGR-WT [32]0.46802.38870.13645.0346
    SE-AMR-WT [40]0.82871.12800.11221.6261
    RCF-MGR-WT [23]0.66361.49520.09553.5651
    LRCF-RWT0.86970.87100.07631.6262
    下载: 导出CSV

    表  2  不同方法对陶瓷晶粒分割的性能指标对比(第二组实验, 镀金的图像)

    Table  2  Performance comparison of different approaches for ceramic grain segmentation (the second group of experiments for gilded image)

    MethodsCV↑VI↓GCE↓BDE↓
    Liu’s-MGR [38]0.26223.80530.35656.9440
    Random Walker [39]0.38232.95170.220216.4378
    SLIC [14]0.32793.09620.407011.3350
    LSC [15]0.33472.84180.32658.0651
    Banerjee’s [30]0.70351.71750.10522.7484
    SE-MGR-WT [32]0.79791.20310.10332.0565
    SE-AMR-WT [40]0.87570.99090.11101.2623
    RCF-MGR-WT [23]0.57711.76910.08954.8813
    LRCF-RWT0.92170.66990.06281.0201
    下载: 导出CSV

    表  3  人工测量晶粒尺寸结果(单位: 像素)

    Table  3  Grain sizes using manual method (Units: pixels)

    测量者1测量者2测量者3测量者4测量者5
    194.5589.1793.3994.2288.51
    290.92100.33105.3891.4899.91
    3107.50100.91102.0996.4989.91
    4101.6189.9192.0894.4293.38
    5108.31103.8895.16102.4593.52
    6112.51108.21112.34109.70107.84
    7101.85104.13102.8094.4089.73
    下载: 导出CSV

    表  4  不同方法对陶瓷晶粒尺寸的计算结果对比(单位: 像素)

    Table  4  Comparison of ceramic grain sizes using different approaches (Units: pixels)

    人工测量Ground TruthLiu’s-MGR [38]RW [39]SLIC [14]LSC [15]Banerjee’s [30]SE-MGR-WT [32]SE-AMR-WT [40]RCF-MGR-WT [23]LRCF-RWT
    192.2697.8088.00195.1674.3363.9592.5848.8883.7363.0798.56
    297.2498.0085.60161.5474.4863.6686.5955.0994.3475.0899.15
    399.8392.3382.81175.1576.6662.39105.2950.9290.5263.0892.47
    493.2993.3465.97206.9675.7262.7386.4553.1787.7065.2192.48
    5100.5096.0974.38192.8075.9968.04102.0267.2593.8759.9596.76
    6110.0898.9369.83177.5676.4870.01104.0876.3896.0059.31100.65
    799.6896.6178.18183.0375.5071.71114.2885.2993.9853.5997.67
    下载: 导出CSV

    表  5  不同方法计算陶瓷晶粒尺寸结果的误差(单位: 像素)

    Table  5  Error comparison of different approaches on ceramic grain size computation (Units: pixels)

    Liu’s-MGR [38]RW [39]SLIC [14]LSC [15]Banerjee’s [30]SE-MGR-WT [32]SE-AMR-WT [40]RCF-MGR-WT [23]LRCF-RWT
    1-9.80+97.36-23.47-33.85-5.22-48.92-14.07-34.73-0.76
    2-12.40+63.54-23.52-34.34-11.41-42.91-3.66-22.92+1.15
    3-9.52+82.82-15.67-29.94-12.96-41.41-1.81-29.25-0.14
    4-27.37+113.62-17.62-30.61-6.89-40.17-5.64-28.13-0.86
    5-21.71+96.71-20.1-28.05+6.07-28.84-2.22-36.14-0.67
    6-29.10+18.63-22.45-28.92+5.15-19.55-2.93-39.62+1.72
    7-18.43+86.42-21.11-24.90+17.67-11.32-2.63-43.02-1.06
    下载: 导出CSV
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  • 收稿日期:  2020-05-06
  • 录用日期:  2020-09-07

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