[1]黄贤山,卢 冶,张小立,等.基于迁移学习的灰铸铁金相组织分类研究[J].计算机技术与发展,2021,31(增刊):21-25.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 004]
　HUANG Xian-shan,LU Ye,ZHANG Xiao-li,et al.Research on Classification of Gray Cast Iron Metallographic Structure Based on Transfer Learning[J].,2021,31(增刊):21-25.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 004]
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基于迁移学习的灰铸铁金相组织分类研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年增刊
页码:
21-25
栏目:
人工智能
出版日期:
2021-12-31

文章信息/Info

Title:
Research on Classification of Gray Cast Iron Metallographic Structure Based on Transfer Learning
文章编号:
1673-629X(2021)S0021-05
作者:
黄贤山1 ; 卢 冶1 ; 张小立2 ; 杨 俊2
1. 江苏科技大学 电气与信息工程学院,江苏 张家港 215600;
2. 江苏科技大学 冶金与材料工程学院,江苏 张家港 215600
Author(s):
HUANG Xian-shan1 ; LU Ye1 ; ZHANG Xiao-li2 ; YANG Jun2
1. School of Electrical and Information Engineering,Jiangsu University of Science and Technology,Zhangjiagang 215600,China;
2. School of Metallurgy and Materials Engineering,Jiangsu University of Science and Technology,Zhangjiagang 215600,China
关键词:
深度学习; 迁移学习; 灰铸铁; 卷积神经网络; VGG16 模型; ResNet50 模型
Keywords:
deep learning; transfer learning; gray cast iron; convolutional neural network; VGG16 model; ResNet50 model
分类号:
TP18;TG115
DOI:
10. 3969 / j. issn. 1673-629X. 2021. S. 004
摘要:
针对灰铸铁金相组织分类问题,提出了基于卷积神经网络和迁移学习的判别方法。 首先收集在显微镜下放大 100倍后的金相组织图像数据,再利用直方图均衡化和高斯滤波等算法对原始图像进行预处理,将处理后的图像数据分为训练集和测试集;然后对已经在源领域训练过的卷积神经网络模型进行微调,并使用经过数据增强的训练集数据对微调后的模型进行再次训练,得到模型最优的参数;最后利用测试集数据评估模型的泛化能力,在实验使用的两种卷积神经网络模型 VGG16 和 ResNet50 上,测试集的预测准确率分别达到了 87. 6% 和 93. 8% 。 该结果表明在灰铸铁金相组织分类的问题上,基于卷积神经网络,并结合迁移学习的方法具有较好的特征提取能力和分类能力,该方法效率高、准确率高,且不需要人为干预,在工业生产中具有广泛的应用前景。
Abstract:
To classify images of gray cast iron metallographic structure,a discrimination method based on convolutional neural network and transfer learning is proposed. Collecting the metallographic tissue image data magnified 100 times under the microscope, and the original images are preprocessed by algorithms such as histogram equalization and Gaussian filtering,after that,a training set and a test set are split apart. Then we fine-tune the pre-trained convolutional neural network model,which has been trained in the source field and dodata augmentation on training set,by which the fine-tuned model is retrained to get the optimal parameters. Finally,the test set is used to evaluate the generalization ability of the model. In the experiment, we apply two widely used convolutional neural network modelsVGG16 and ResNet50, the prediction accuracy of the test set reaches 87. 6% and 93. 8% . It is shown that on the problem of metallographic structure classification of cast iron,the method based on the convolutional neural network and combined with the transfer learning has better feature extraction and classification capabilities. This method has high efficiency and high accuracy,mean while it does not require human intervention,which has broad application prospects in industrial production.

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更新日期/Last Update: 2021-09-10