ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images

CAI Chao-li; LI Chun-chun; HUANG Lin; YANG Tie-jun

doi:10.12263/DZXB.20201416

您当前的位置：

首页 >

文章列表页 >

ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images

PAPERS | 更新时间：2025-12-08

- ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images
- ACTA ELECTRONICA SINICA Vol. 50, Issue 2, Pages: 461-469(2022)
- 作者机构：
  
  1.桂林理工大学广西嵌入式技术与智能系统重点实验室,广西桂林 541006
  2.桂林理工大学材料科学与工程学院,广西桂林 541006
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201416
  CLC： TP389.1
- Received：11 December 2020，
  
  Revised：2021-10-19，
  
  Published：25 February 2022
- 稿件说明：
移动端阅览
蔡超丽,李纯纯,黄琳等.ED-NAS: 基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法[J].电子学报,2022,50(02):461-469.

CAI Chao-li,LI Chun-chun,HUANG Lin,et al.ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images[J].ACTA ELECTRONICA SINICA,2022,50(02):461-469.
蔡超丽,李纯纯,黄琳等.ED-NAS: 基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法[J].电子学报,2022,50(02):461-469. DOI： 10.12263/DZXB.20201416.

CAI Chao-li,LI Chun-chun,HUANG Lin,et al.ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images[J].ACTA ELECTRONICA SINICA,2022,50(02):461-469. DOI： 10.12263/DZXB.20201416.

摘要

为了提高深度卷积神经网络（Convolutional Neural Network，CNN）设计的自动化程度并进一步提高陶瓷晶粒扫描电子显微镜（Scanning Electron Microscope，SEM）图像分割的准确性，提出了一种基于神经网络架构搜索的陶瓷晶粒图像分割方法.该方法设计多分支结构编码空间和链式结构解码空间，并构造多分支结构编码Cell和链式结构解码Cell；同时基于强化学习分别搜索最佳编码Cell和解码Cell；此外，基于编码-解码神经网络架构堆叠最佳Cell构建陶瓷晶粒图像分割CNN，并采用池化索引在解码阶段恢复丢失的细节信息.实验在包含了629张的陶瓷晶粒SEM图像数据集上进行，搜索最佳Cell耗时约148 GPU-时.与U-Net、SegNet等SOTA方法相比，该方法在陶瓷晶粒测试集上获得了更高的分割准确性（mIoU

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=39108635&type=

http://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=39108628&type=

1.60866666

2.28600001

68.9%）.

Abstract

In order to improve the automation degree of deep convolutional neural network(CNN) design and further improve the accuracy of ceramic grainsegmentation using scanning electron microscope(SEM) images

a ceramic grain segmentation method is proposed based on neural architecture search. This method designs searching spaces including the ones of multi-branch structure for encoding and chain structure for decoding

where encoding cells(E-cell) and decoding cells(D-cell) are constructed. The best E-cell and D-cell are found using reinforcement learning. Moreover

an encoding-decoding neural architecture-based CNN is built for ceramic grain segmentation by stacking the best cells

and the pooling indices are adopted to recover the lost details in the decoding stage. The experiment was carried out on a dataset of 629 SEM images of ceramic grain

and the searching process took about 148 GPU-hours. Compared with SOTA methods such as U-Net and SegNet

the proposed method obtained higher segmentation accuracy(mIoU≈68.9%) on a ceramic grains test dataset.

关键词

Keywords

references

张伟儒 , 李伶 , 王坤 . 先进陶瓷材料研究现状及发展趋势 [J]. 新材料产业 , 2016 , ( 1 ): 2 - 8 .

ZHANG Weiru , LI Ling , WANG Kun . Research status and development trend of advanced ceramic materials [J]. New Materials Industry , 2016 , ( 1 ): 2 - 8 . (in Chinese)

CHEN Y W , MOUSSI J , DRURY J L , et al . Zirconia in biomedical applications [J]. Expert Rev Med Devices , 2016 : 945 - 963 .

VIOLA G , CHONG K B , ERIKSSON M , et al . Effect of grain size on domain structures, dielectric and thermal depoling of Nd-substituted bismuth titanate ceramics [J]. Applied Physics Letters , 2013 , 103 ( 18 ): 899 .

雷涛 , 李云彤 , 周文政 , 等 . 数据与模型联合驱动的陶瓷材料晶粒分割 [J]. 自动化学报 , 2020 , 46(x): 1- 16 . DOI: 10.16383/j.aas.c200277 http://dx.doi.org/10.16383/j.aas.c200277 .

LEI Tao , LI Yuntong , ZHOU Wenzheng , et al . Grain segmentation of ceramic materials using data-driven jointing model-driven [J]. Acta Automatica Sinica , 2020 , 46(x): 1- 16 . DOI: 10.16383/j.aas.c200277. http://dx.doi.org/10.16383/j.aas.c200277. (in Chinese)

BODNAR J L , NICOLAS J L , CANDORÉ J C . Non-destructive testing by infrared thermography under random excitation and ARMA analysis [J]. International Journal of Thermophysics , 2012 , 33 ( 10-11 ): 2011 - 2015 .

ZHANG L X , XU Z , WEI S , et al . Grain size automatic determination for 7050 Al alloy based on a fuzzy logic method [J]. Rare Metal Materials and Engineering , 2016 , 45 : 548 - 554 .

LI Q , NI X , LIU G . Ceramic image processing using the second curvelet transform and watershed algorithm [C]// IEEE International Conference on Robotics and Biomimetics(ROBIO) . Sanya, China : IEEE , 2007 : 2037 - 2042 .

VINCENT L , SOILLE P . Watersheds in digital spaces: An efficient algorithm based on immersion simulations [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 1991 , 13 ( 6 ): 583 - 598 .

HEILBRONNER R . Automatic grain boundary detection and grain size analysis using polarization micrographs or orientation images [J]. Journal of Structural Geology , 2000 , 22 : 969 - 981 .

SCHMIDHUBER J . Deep learning in neural networks: An overview [J]. Neural networks , 2015 , 61 : 85 - 117 .

RONNEBERGER O , FISCHER P , BROX T . U-Net: Convolutional networks for biomedicalimage segmentation [EB/OL]. ( 2015-05-18 ). https://arxiv.org/abs/1505.04597 https://arxiv.org/abs/1505.04597 .

LONG J , SHELHAMER E , DARRELL T . Fully convolutional networks for semantic segmentation [C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR) . Boston, MA, USA : IEEE , 2015 : 3431 - 3440 .

JIANG F , GU Q , HAO H , et al . Feature extraction and grain segmentation of sandstone images based on convolutional neural networks [C]// 24th International Conference on Pattern Recognition(ICPR) . Beijing, China : IEEE , 2018 : 2636 - 2641 .

MA B , LIU C , WEI X , et al . WPU-Net: Boundary learning by usi-ng weighted propagation in convolution network [EB/OL]. ( 2019-05-22 ). https://arxiv.org/abs/1905.09226 https://arxiv.org/abs/1905.09226 .

LIU Y , CHENG MM , HU X , et al . Richer convolutional features for edge detection [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Honolulu, HI, USA : IEEE , 2017 : 3000 - 3009 .

ZOPH B , LEQ V . Neural architecture search with reinforcement learning [EB/OL]. https://arxiv.org/abs/1611.015 https://arxiv.org/abs/1611.015 78 , 2017-02-15 .

LIU C , CHEN LC , SCHROFF F , et al . Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA, USA : IEEE , 2019 : 82 - 92 .

ZHANG Y , QIU Z , LIU J , et al . Customizable Architecture Search for Semantic Segmentation [C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR) . Long Beach, CA, USA : IEEE , 2019 : 11633 - 11642 .

ELSKEN T , METZEN J H , HUTTER F . Neural architecture search: A survey [EB/OL]. ( 2019-04-26 ). https://arxiv.org/abs/1808.05377 https://arxiv.org/abs/1808.05377 .

ZHONG Z , YAN J , WU W , et al . Practical block-wise neural network architecture generation [C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City, UT, USA : IEEE , 2018 : 2423 - 2432 .

ZOPH B , VASUDEVAN V , SHLENS J , et al . Learning transferable architectures for scalable image recognition [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition . Salt Lake City, UT, USA : IEEE , 2018 : 8697 - 8710 .

CHEN LC , ZHU Y , PAPANDREOU G , et al . Encoder-decoder with atrous separable convolution for semantic image segmentation [C]// Proceedings of the European Conference on Computer Vision(ECCV) . Munich : Springer , 2018 : 801 - 818 .

ELSKEN T , METZEN J H , HUTTER F . Simple and efficient architecture search for convolutional neural networks [EB/OL]. ( 2017-11-13 ). https://arxiv.org/abs/1711.04528 https://arxiv.org/abs/1711.04528 .

BAKER B , GUPTA O , NAIK N , et al . Designing neural network architectures using reinforcement learning [EB/OL]. ( 2017-03-22 ). https://arxiv.org/abs/1611.02167 https://arxiv.org/abs/1611.02167 .

HE K , ZHANG X , REN S , et al . Deep residual learning for image recognition [C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR) . Seattle, WA, USA : IEEE , 2016 : 770 - 778 .

REAL E , AGGARWAL A , HUANG Y , et al . Regularized evolution for image classifier architecture search [EB/OL]. ( 2019-02-16 ). https://arxiv.org/abs/1802.01548 https://arxiv.org/abs/1802.01548 .

LIU C , ZOPH B , SHLENS J , et al . Progressive neural architecture search [EB/OL]. ( 2018-07-26 ). https://arxiv.org/abs/1712.00559 https://arxiv.org/abs/1712.00559 .

NOH H , HONG S , HAN B . Learning deconvolution network for semantic segmentation [C]// IEEE International Conference on Computer Vision(ICCV) . Santiago, Chile : IEEE , 2015 : 1520 - 1528 .

BADRINARAYANAN V , KENDALL A , CIPOLLA R . SegNet: A deep convolutional encoder-decoder architecture for image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017 , 39 : 2481 - 2495 .

PHAM H , GUAN M Y , ZOPH B , et al . Efficient neural architecture search via parameter sharing [EB/OL]. ( 2018-02-12 ). https://arxiv.org/abs/1802.03268 https://arxiv.org/abs/1802.03268

ZHAO H , SHI J , QI X , et al . Pyramid scene parsing network [C]// IEEE Conference on Computer Vision and Pattern Recognition(CVPR) . Honolulu, HI, USA : IEEE , 2017 : 6230 - 6239 .

ZAREMBA W , SUTSKEVER I , VINYALS O . Recurrent neural network regularization [EB/OL]. ( 2015-02-19 ). https://arxiv.org/abs/1409.2329 https://arxiv.org/abs/1409.2329 .

SUTTON R S , MCALLESTER D , SINGH S , et al . Policy gradient methods for reinforcement learning with function approximation [J]. Advances in Neural Information Processing Systems , 1999 , 12 : 1057 - 1063 .

ROMERA E , ALVAREZ J M , BERGASA L M , et al . ERFNet: Efficient residual factorized convnet for real-time semantic segmentation [J]. IEEE Transactions on Intelligent Transportation Systems , 2018 , 19 ( 1 ): 263 - 272 .

PASZKE A , CHAURASIA A , KIM S , et al . ENet: A deep neural network architecture for real-time semantic segmentation [EB/OL].( 2016-06-07 ). https://arxiv.org/abs/1606.02147 https://arxiv.org/abs/1606.02147 .

CHEN LC , PAPANDREOU G , SCHROFF F , et al . Rethinking atrous convolution for semantic image segmentation [EB/OL]. ( 2017-12-05 ). https://arxiv.org/abs/1706.05587 https://arxiv.org/abs/1706.05587 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information

SwinT-Unet: Ultrasound Image Segmentation Based on Two-Channel Self-Attention Mechanism

A Variational Level Set Model Based on Beer-Lambert Law

New Theories and Methods of Image Segmentation

Related Author

Chao-li CAI

Chun-chun LI

Lin HUANG

Tie-jun YANG

WANG Xiao-peng

WANG Hai-zhou

CHEN Hao-ran

SONG Yan-tao

Related Institution

Guangxi Key Laboratory of Embedded Technology and Intelligent System， Guilin University of Technology

School of Materials Science and Engineering， Guilin University of Technology

School of Electronic and Information Engineering, Lanzhou Jiaotong University

Institute of Big Data Science & Industry, Shanxi University

School of Computer & Information Technology, Shanxi University

⁰