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

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

1.
陕西科技大学电子信息与人工智能学院西安 710021
2.
陕西科技大学电气与控制工程学院西安 710021
3.
陕西科技大学材料科学与工程学院西安 710021
4.
陕西科技大学文理学院西安 710021

基金项目: 国家自然科学基金(61871259, 61861024), 陕西省杰出青年科学基金(2021JC-47), 陕西省重点研发计划(2022GY-436, 2021ZDLGY08-07), 陕西省创新能力支撑计划(2020SS-03)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和模式识别与机器学习. 本文通信作者.E-mail: 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.E-mail: 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.E-mail: 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. 2018年获西安交通大学电子科学与技术专业博士学位. 主要研究方向为新型储能电介质材料与器件,柔性可穿戴材料与器件和材料微纳尺度结构解析.E-mail: yuanqibin-sust@163.com

王成兵：陕西科技大学材料科学与工程学院教授. 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.E-mail: wangchengbing@gmail.com

张小红：陕西科技大学文理学院教授. 2005年获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.E-mail: zhangxiaohong@sust.edu.cn

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出版历程
- 收稿日期: 2020-05-06
- 录用日期: 2020-09-07
- 网络出版日期: 2022-04-13
- 刊出日期: 2022-04-13

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

1.
School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi＇an 710021
2.
School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi＇an 710021
3.
School of Material Science and Engineering, Shaanxi University of Science and Technology, Xi＇an 710021
4.
School of Arts and Sciences, Shaanxi University of Science and Technology, Xi＇an 710021

Funds: Supported by National Natural Science Foundation of China (61871259, 61861024), Natural Science Basic Research Program of Shaanxi (2021JC-47), Key Research and Development Program of Shaanxi (2022GY-436, 2021ZDLGY08-07), Shaanxi Joint Laboratory of Artificial Intelligence (2020SS-03)

More Information

Author Bio:
LEI Tao　Professor at the School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology. He received his Ph.D. 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

LI Yun-Tong　Master student at the School of Electrical and Control Engineering, Shaanxi University of Science and Technology. She received her bachelor degree in automation from Shaanxi University of Science and Technology in 2018. Her main research interest is image processing

ZHOU Wen-Zheng　Master student at the School of Electrical and Control Engineering, Shaanxi University of Science and Technology. He received his bachelor degree in automation from Chongqing University in 2017. His main research interest is image processing

YUAN Qi-Bin　Associate professor at the School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology. He received his Ph.D. degree in electronics science and technology from Xi＇an JiaoTong University in 2018. His research interest covers dielectric materials and devices for energy storage applications, wearable materials and devices, and material structures at the micro- and nano-scale

WANG Cheng-Bing　Professor at the School of Materials Science and Engineering, Shaanxi University of Science and Technology. He received his Ph.D. degree in physical chemistry from Lanzhou Institute of Chemical Physics, Chinese Academy of Sciences in 2008. His research interest covers material surface technology and coating

ZHANG Xiao-Hong　Professor at the School of Arts and Sciences, Shaanxi University of Science and Technology. He received his Ph.D. degree in computer software and theory from Northwestern Polytechnical University in 2005. His research interest covers fuzzy logic, rough sets, mathematics of uncertainty, data science and artificial intelligence

摘要

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; 最后提出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, 该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, 该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.
- 图像分割 /
- 卷积神经网络 /
- 鲁棒分水岭变换 /
- 特征融合
Abstract: The research of the distribution of ceramic grain sizes is significant for estimating physical properties of ceramic materials. At present, researchers mainly use manual methods to measure ceramic grains, which leads to low efficiency and large error due to the irregular shape and different sizes of grains in ceramic images. To solve this issue, a new algorithm used for grain segmentation of ceramic materials is proposed in this paper. This algorithm first performs image pre-processing to solve the problem of intensity inhomogeneity caused by surface reflection of materials in the imaging process. Secondly, a robust watershed transform (RWT) is presented and used for pre-segmentation of grains in scanning electron microscope images, which solves two problems existing in traditional watershed transform, one is over-segmentation and the other is the difficulty of balancing the number of segmentation regions and the accuracy of boundaries. Finally, we present a lightweight and richer convolutional features network (LRCF) used for grain contour prediction, and use the results generated by LRCF to optimize the pre-segmentation result. Compared with popular image segmentation algorithms, the proposed algorithm shows two advantages. On one hand, it achieves more accurate grain region segmentation due to the employment of RWT; on the other hand, it provides more accurate grain boundaries due to the optimization of LRCF. Experimental results show that the proposed algorithm not only provides accurate grain sizes of ceramic materials, but also shows high calculation efficiency, which is significant for property analysis of ceramic materials.
- Image segmentation /
- convolutional neural network (CNN) /
- robust watershed transform /
- feature fusion
注释:

1) 收稿日期 2020-05-06 录用日期 2020-09-07 Manuscript received May 6, 2020; accepted September 7, 2020 国家自然科学基金(61871259, 61861024), 陕西省杰出青年科学基金(2021JC-47), 陕西省重点研发计划(2022GY-436, 2021ZDLGY08-07), 陕西省创新能力支撑计划(2020SS-03)资助 Supported by National Natural Science Foundation of China (61871259, 61861024), Natural Science Basic Research Program of Shaanxi (2021JC-47), Key Research and Development Program of Shaanxi (2022GY-436, 2021ZDLGY08-07), Shaanxi Joint Laboratory of Artificial Intelligence (2020SS-03) 本文责任编委胡清华 Recommended by Associate Editor HU Qing-Hua 1. 陕西科技大学电子信息与人工智能学院西安 710021 2. 陕西科技大学电气与控制工程学院西安 710021 3. 陕西科技大学

2) 材料科学与工程学院西安 710021 4. 陕西科技大学文理学院西安 710021 1. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi＇an 710021 2. School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi＇an 710021 3. School of Material Science and Engineering, Shaanxi University of Science and Technology, Xi＇an 710021 4. School of Arts and Sciences, Shaanxi University of Science and Technology, Xi＇an 710021

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图 1 总体流程图

Fig. 1 Overall flow chart

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图 2 原图与预处理结果对比

Fig. 2 Comparison on original and pre-processed images

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图 3 不同参数的MGR-WT对图像的分割结果对比

Fig. 3 Segmentation results comparison using MRG-WT with different values of r

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图 4 RWT与MGR-WT对陶瓷材料晶粒的分割结果对比

Fig. 4 Comparison of the segmentation results of ceramic grains between RWT and MGR-WT

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图 5 基于RWT的图像分割结果

Fig. 5 RWT suffer from the problem of double line contour

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图 6 去除双线

Fig. 6 Removing double lines

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图 7 深度可分离卷积

Fig. 7 Depthwise separable convolution

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图 8 LRCF网络结构图

Fig. 8 LRCF network structure

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图 9 基于LRCF与分水岭变换的图像分割

Fig. 9 Image segmentation using the combination of LRCF and watershed transform

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图 10 轮廓优化

Fig. 10 Contour optimization

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图 11 第一组分割结果对比(未镀金图像)

Fig. 11 Comparison of the first group of segmentation results (unplated image)

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图 12 第二组分割结果对比(镀金图像)

Fig. 12 Comparison of the first group of segmentation results (gilded image)

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表 1 不同方法对陶瓷晶粒分割的性能指标对比(第1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)

Methods	CV↑	VI↓	GCE↓	BDE↓
Liu＇s-MGR^[³⁸^]	0.2889	3.4270	0.4742	7.3230
Random Walker^[³⁹^]	0.3556	2.9003	0.1407	13.2147
SLIC^[¹⁴^]	0.3547	3.0524	0.4396	10.1678
LSC^[¹⁵^]	0.3455	2.8820	0.3563	7.5911
Banerjee＇s^[³⁰^]	0.5959	2.1992	0.2031	3.9182
SE-MGR-WT^[³²^]	0.4680	2.3887	0.1364	5.0346
SE-AMR-WT^[⁴⁰^]	0.8287	1.1280	0.1122	1.6261
RCF-MGR-WT^[²³^]	0.6636	1.4952	0.0955	3.5651
LRCF-RWT	0.8697	0.8710	0.0763	1.6262

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表 2 不同方法对陶瓷晶粒分割的性能指标对比(第2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)

Methods	CV↑	VI↓	GCE↓	BDE↓
Liu＇s-MGR^[³⁸^]	0.2622	3.8053	0.3565	6.9440
Random Walker^[³⁹^]	0.3823	2.9517	0.2202	16.4378
SLIC^[¹⁴^]	0.3279	3.0962	0.4070	11.3350
LSC^[¹⁵^]	0.3347	2.8418	0.3265	8.0651
Banerjee＇s^[³⁰^]	0.7035	1.7175	0.1052	2.7484
SE-MGR-WT^[³²^]	0.7979	1.2031	0.1033	2.0565
SE-AMR-WT^[⁴⁰^]	0.8757	0.9909	0.1110	1.2623
RCF-MGR-WT^[²³^]	0.5771	1.7691	0.0895	4.8813
LRCF-RWT	0.9217	0.6699	0.0628	1.0201

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表 3 人工测量晶粒尺寸结果(像素)

Table 3 Grain sizes using manual method (pixels)

	测量者 1	测量者 2	测量者 3	测量者 4	测量者 5
1	94.55	89.17	93.39	94.22	88.51
2	90.92	100.33	105.38	91.48	99.91
3	107.50	100.91	102.09	96.49	89.91
4	101.61	89.91	92.08	94.42	93.38
5	108.31	103.88	95.16	102.45	93.52
6	112.51	108.21	112.34	109.70	107.84
7	101.85	104.13	102.80	94.40	89.73

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表 4 不同方法对陶瓷晶粒尺寸的计算结果对比(像素)

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

	人工测量	Ground Truth	Liu＇s-MGR^[³⁸^]	RW^[³⁹^]	SLI^[¹⁴^]	LSC^[¹⁵^]	[30]	SE-MGR-WT^[³²^]	SE-AMR-WT^[⁴⁰^]	RCF-MGR-WT^[²³^]	LRCF-RWT
1	92.26	97.80	88.00	195.16	74.33	63.95	92.58	48.88	83.73	63.07	98.56
2	97.24	98.00	85.60	161.54	74.48	63.66	86.59	55.09	94.34	75.08	99.15
3	99.83	92.33	82.81	175.15	76.66	62.39	105.29	50.92	90.52	63.08	92.47
4	93.29	93.34	65.97	206.96	75.72	62.73	86.45	53.17	87.70	65.21	92.48
5	100.50	96.09	74.38	192.80	75.99	68.04	102.02	67.25	93.87	59.95	96.76
6	110.08	98.93	69.83	177.56	76.48	70.01	104.08	76.38	96.00	59.31	100.65
7	99.68	96.61	78.18	183.03	75.50	71.71	114.28	85.29	93.98	53.59	97.67

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表 5 不同方法计算陶瓷晶粒尺寸结果的误差(像素)

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

	Liu＇s-MGR^[³⁸^]	RW^[³⁹^]	SLIC^[¹⁴^]	LSC^[¹⁵^]	[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

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