[1]王佳锐,刘能锋,曲鹏.卷积神经网络金相组织自动识别[J].智能系统学报,2022,17(4):698-706.[doi:10.11992/tis.202110035]
　WANG Jiarui,LIU Nengfeng,QU Peng.Automatic identification of metallographic structure based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2022,17(4):698-706.[doi:10.11992/tis.202110035]
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卷积神经网络金相组织自动识别

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 17 期数: 2022年第4期 页码: 698-706 栏目: 学术论文—机器学习 出版日期: 2022-07-05
Title:
Automatic identification of metallographic structure based on convolutional neural network
作者:
王佳锐1,2, 刘能锋2, 曲鹏1
1. 廊坊燕京职业技术学院 机电工程系，河北 廊坊 065200;
2. 哈尔滨工业大学 实验与创新实践教育中心，广东 深圳 518055
Author(s):
WANG Jiarui1,2, LIU Nengfeng2, QU Peng1
1. Mechanical and Electronic Engineering Department, Langfang Yanjing Polytechnic Inst., Langfang 065200, China;
2. Education Center of Experiments and Innovations, Harbin Institute of Technology, Shenzhen 518055, China
关键词:
卷积神经网络; 金相组织; 图像处理; 网络模型; 自动辨识; LeNet神经网络; AlexNet神经网络; VGGNet神经网络
Keywords:
convolutional neural network; metallographic structure; image processing; network model; automatic identification; LeNet neural network; AlexNet neural network; VGGNet neural network
分类号:
TG141;TP391.4;TP183
DOI:
10.11992/tis.202110035
摘要:
为了降低人工分辨金相组织图像类别的误差率，提高分辨效率，采用卷积神经网络模型对金相组织图像进行自动辨识。对制备金相样块所得铁素体与马氏体两种金相组织图像进行分析，提出符合金相组织图像分布特征的预处理方案。通过采用图像尺寸归一化、灰度值归一化以及高斯平滑处理等方法，对原始金相组织图像进行预处理，建立金相组织图像数据集。针对建立的铁素体和马氏体金相组织图像数据集，提出了适合金相组织图像辨识的改进模型，分别记为LeNet-MetStr模型、AlexNet-MetStr模型和VGGNet-MetStr模型。对3种改进卷积神经网络进行模型训练及分析，结果表明VGGNet-MetStr模型对2种金相组织图像自动辨识具有更高的准确度。
Abstract:
The convolutional neural model was used to automatically identify metallographic structure images to reduce the error rate of manual resolution of metallographic structure image categories and improve the resolution efficiency. Two kinds of metallographic structure images of ferrite and martensite obtained from metallographic sample blocks were analyzed, and a preprocessing scheme conforming to the distribution characteristics of the metallographic structure image was proposed. Image size normalization, gray value normalization, and Gaussian smoothing are used to establish the metallographic image sample set and training set. Aiming at the established image data sets of two types of metallographic structures such as ferrite and martensite, the improved models suitable for metallographic structure image recognition are proposed, which are named the LeNet-MetStr model, AlexNet-MetStr model, and VGGNet-MetStr model, respectively. Three improved convolutional neural networks were trained and analyzed. The results show that the VGGNet-MetStr model has higher accuracy for the automatic identification of two kinds of metallographic structure images.
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备注/Memo

收稿日期:2021-10-29。
基金项目:国家自然科学基金项目（52161004）；2021年廊坊市科技局高新技术项目（2021011018）.
作者简介:王佳锐，讲师，主要研究方向为机器视觉、人工智能、深度学习算法应用;刘能锋，副教授，主要研究方向为机器人控制、教学平台设计;曲鹏，讲师，主要研究方向为金属材料制备与表征、高熵合金力学性能与检测、锆钛合金力学性能与检测
通讯作者:曲鹏. E-mail：372292920@qq.com

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