ED-NAS: 基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法

蔡超丽; 李纯纯; 黄琳; 杨铁军

doi:10.12263/DZXB.20201416

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ED-NAS: 基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法

学术论文 | 更新时间：2025-12-08

- ED-NAS: 基于神经网络架构搜索的陶瓷晶粒SEM图像分割方法
- ED-NAS: Ceramic Grain Segmentation Based on Neural Architecture Search Using SEM Images
- 电子学报 2022年50卷第2期页码：461-469
- 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：
  
  [ "蔡超丽　女，1996年11月出生，湖北十堰人.现为桂林理工大学信息学院硕士研究生，主要研究方向为计算机视觉、深度学习.E‑mail: kiko.c@qq.com" ]
  [ "杨铁军（通讯作者）　男，1979年6月出生，湖南宁乡人.桂林理工大学信息学院教授，硕士生导师，研究方向为计算机视觉、深度学习.E‑mail: yattie@foxmail.com" ]
- 基金信息：
  
  国家自然科学基金(61941202);广西自然科学基金(2018GXNSFBA281081);广西嵌入式技术与智能系统重点实验室开放基金(2020-2-2)
- DOI：10.12263/DZXB.20201416
  中图分类号： TP389.1
- 收稿：2020-12-11，
  
  修回：2021-10-19，
  
  纸质出版：2022-02-25
- 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

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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

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