Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (8): 2593-2601.DOI: 10.11772/j.issn.1001-9081.2022060893
• Multimedia computing and computer simulation • Previous Articles Next Articles
Xueyu HUANG1,2, Huaiyu HE1, Huimin LIN1, Jinshui CHEN3,4()
Received:
2022-06-20
Revised:
2022-09-05
Accepted:
2022-09-09
Online:
2022-09-22
Published:
2023-08-10
Contact:
Jinshui CHEN
About author:
HUANG Xueyu, born in 1970, Ph. D., professor. His research interests include enterprise informatization, smart factory.Supported by:
通讯作者:
陈金水
作者简介:
黄学雨(1970—),男,江西赣州人,教授,博士,主要研究方向:企业信息化、智能工厂基金资助:
CLC Number:
Xueyu HUANG, Huaiyu HE, Huimin LIN, Jinshui CHEN. Classification and recognition method of copper alloy metallograph based on feature aggregation[J]. Journal of Computer Applications, 2023, 43(8): 2593-2601.
黄学雨, 贺怀宇, 林慧敏, 陈金水. 基于特征聚合的铜合金金相图分类识别方法[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2593-2601.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060893
数据集类型 | 不同成分的铜合金 | |||
---|---|---|---|---|
Cu-Cr-Zr | Cu-Fe | Cu-Fe-Cr | Cu-Fe-Mg | |
训练集 | 1 020 | 878 | 824 | 324 |
验证集 | 348 | 296 | 276 | 108 |
测试集 | 336 | 272 | 266 | 90 |
Tab. 1 Distribution of pre-processed dataset
数据集类型 | 不同成分的铜合金 | |||
---|---|---|---|---|
Cu-Cr-Zr | Cu-Fe | Cu-Fe-Cr | Cu-Fe-Mg | |
训练集 | 1 020 | 878 | 824 | 324 |
验证集 | 348 | 296 | 276 | 108 |
测试集 | 336 | 272 | 266 | 90 |
阶段 | 输出大小 | ResNet-CBAM |
---|---|---|
Stage0:Conv 1 | 224 | 3 |
Stage1:CBMA | 224 | 1 1 |
224 | 7 | |
Stage2:Conv2 | 224 | 3 |
Stage3:Conv3 | 112 | |
Stage4:Conv4 | 56 | |
Stage5:Conv5 | 28 | |
Stage6:CBMA | 28 | 1 1 |
28 | 7 | |
Stage7 | 1 | Average pool,1 024 -d fc |
Tab. 2 ResNet-CBAM network parameters
阶段 | 输出大小 | ResNet-CBAM |
---|---|---|
Stage0:Conv 1 | 224 | 3 |
Stage1:CBMA | 224 | 1 1 |
224 | 7 | |
Stage2:Conv2 | 224 | 3 |
Stage3:Conv3 | 112 | |
Stage4:Conv4 | 56 | |
Stage5:Conv5 | 28 | |
Stage6:CBMA | 28 | 1 1 |
28 | 7 | |
Stage7 | 1 | Average pool,1 024 -d fc |
模型 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
ResNet50 | 95.746 | 96.151 | 95.324 | 95.735 |
ResNet101 | 96.784 | 97.025 | 97.375 | 97.199 |
DenseNet121 | 97.925 | 98.213 | 98.325 | 98.162 |
Xception | 96.887 | 97.174 | 96.875 | 97.024 |
ResNet-CBAM | 97.510 | 97.525 | 97.662 | 97.662 |
Tab. 3 Performance comparison of different convolutional neural network models on test set
模型 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
ResNet50 | 95.746 | 96.151 | 95.324 | 95.735 |
ResNet101 | 96.784 | 97.025 | 97.375 | 97.199 |
DenseNet121 | 97.925 | 98.213 | 98.325 | 98.162 |
Xception | 96.887 | 97.174 | 96.875 | 97.024 |
ResNet-CBAM | 97.510 | 97.525 | 97.662 | 97.662 |
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
GLCM-KNN | 81.327 | 81.988 | 79.831 | 79.995 |
GLCM-DT | 73.236 | 73.804 | 72.788 | 73.144 |
GLCM-SVM | 92.738 | 93.714 | 93.234 | 93.412 |
Tab. 4 Performance comparison of different GLCM-based classification methods on test set
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
GLCM-KNN | 81.327 | 81.988 | 79.831 | 79.995 |
GLCM-DT | 73.236 | 73.804 | 72.788 | 73.144 |
GLCM-SVM | 92.738 | 93.714 | 93.234 | 93.412 |
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
ResNet50-KNN | 98.237 | 97.893 | 97.362 | 97.617 |
ResNet50-DT | 96.577 | 95.958 | 96.218 | 96.079 |
ResNet50-SVM | 98.340 | 98.412 | 98.178 | 98.278 |
ResNet101-KNN | 98.432 | 97.981 | 98.241 | 98.102 |
ResNet101-DT | 97.303 | 97.141 | 97.692 | 97.379 |
ResNet101-SVM | 98.651 | 98.114 | 98.235 | 98.171 |
ResNet-CBAM-KNN | 98.651 | 98.635 | 98.483 | 98.552 |
ResNet-CBAM-DT | 96.888 | 97.292 | 96.732 | 96.992 |
ResNet-CBAM-SVM | 98.444 | 98.445 | 98.441 | 98.440 |
DenseNet121-KNN | 98.548 | 98.313 | 98.313 | 98.408 |
DenseNet121-DT | 96.058 | 95.374 | 96.266 | 95.732 |
DenseNet121-SVM | 98.548 | 98.534 | 98.543 | 98.521 |
Xception-KNN | 98.029 | 97.272 | 98.289 | 97.742 |
Xception-DT | 96.680 | 96.450 | 95.893 | 96.130 |
Xception-SVM | 98.029 | 97.644 | 98.300 | 97.950 |
Tab. 5 Comparison of different classification methods based on convolutional features on test set
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
ResNet50-KNN | 98.237 | 97.893 | 97.362 | 97.617 |
ResNet50-DT | 96.577 | 95.958 | 96.218 | 96.079 |
ResNet50-SVM | 98.340 | 98.412 | 98.178 | 98.278 |
ResNet101-KNN | 98.432 | 97.981 | 98.241 | 98.102 |
ResNet101-DT | 97.303 | 97.141 | 97.692 | 97.379 |
ResNet101-SVM | 98.651 | 98.114 | 98.235 | 98.171 |
ResNet-CBAM-KNN | 98.651 | 98.635 | 98.483 | 98.552 |
ResNet-CBAM-DT | 96.888 | 97.292 | 96.732 | 96.992 |
ResNet-CBAM-SVM | 98.444 | 98.445 | 98.441 | 98.440 |
DenseNet121-KNN | 98.548 | 98.313 | 98.313 | 98.408 |
DenseNet121-DT | 96.058 | 95.374 | 96.266 | 95.732 |
DenseNet121-SVM | 98.548 | 98.534 | 98.543 | 98.521 |
Xception-KNN | 98.029 | 97.272 | 98.289 | 97.742 |
Xception-DT | 96.680 | 96.450 | 95.893 | 96.130 |
Xception-SVM | 98.029 | 97.644 | 98.300 | 97.950 |
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
GLCM+ResNet50-KNN | 98.237 | 97.760 | 97.706 | 97.732 |
GLCM+ResNet50-DT | 97.822 | 97.495 | 97.538 | 97.506 |
GLCM+ResNet50-SVM | 98.859 | 98.703 | 98.615 | 98.655 |
GLCM+ResNet101-KNN | 98.444 | 98.449 | 98.513 | 98.475 |
GLCM+ResNet101-DT | 96.058 | 95.426 | 95.823 | 95.580 |
GLCM+ResNet101-SVM | 98.859 | 98.819 | 98.619 | 98.712 |
GLCM+ResNet-CBAM-KNN | 98.652 | 98.055 | 98.853 | 98.441 |
GLCM+ResNet-CBAM-DT | 96.992 | 97.401 | 97.005 | 97.182 |
GLCM+ResNet-CBAM-SVM(本文方法) | 98.963 | 99.077 | 98.927 | 98.996 |
GLCM+DenseNet121-KNN | 98.652 | 98.794 | 98.637 | 98.700 |
GLCM+DenseNet121-DT | 95.954 | 96.498 | 96.394 | 96.361 |
GLCM+DenseNet121-SVM | 98.963 | 98.832 | 98.895 | 98.856 |
GLCM+Xception-KNN | 97.925 | 97.178 | 98.198 | 97.650 |
GLCM+Xception-DT | 97.199 | 96.691 | 97.291 | 96.979 |
GLCM+Xception-SVM | 98.029 | 97.644 | 98.300 | 97.950 |
Tab. 6 Performance comparison of different classification methods based on aggregated features on test set
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
GLCM+ResNet50-KNN | 98.237 | 97.760 | 97.706 | 97.732 |
GLCM+ResNet50-DT | 97.822 | 97.495 | 97.538 | 97.506 |
GLCM+ResNet50-SVM | 98.859 | 98.703 | 98.615 | 98.655 |
GLCM+ResNet101-KNN | 98.444 | 98.449 | 98.513 | 98.475 |
GLCM+ResNet101-DT | 96.058 | 95.426 | 95.823 | 95.580 |
GLCM+ResNet101-SVM | 98.859 | 98.819 | 98.619 | 98.712 |
GLCM+ResNet-CBAM-KNN | 98.652 | 98.055 | 98.853 | 98.441 |
GLCM+ResNet-CBAM-DT | 96.992 | 97.401 | 97.005 | 97.182 |
GLCM+ResNet-CBAM-SVM(本文方法) | 98.963 | 99.077 | 98.927 | 98.996 |
GLCM+DenseNet121-KNN | 98.652 | 98.794 | 98.637 | 98.700 |
GLCM+DenseNet121-DT | 95.954 | 96.498 | 96.394 | 96.361 |
GLCM+DenseNet121-SVM | 98.963 | 98.832 | 98.895 | 98.856 |
GLCM+Xception-KNN | 97.925 | 97.178 | 98.198 | 97.650 |
GLCM+Xception-DT | 97.199 | 96.691 | 97.291 | 96.979 |
GLCM+Xception-SVM | 98.029 | 97.644 | 98.300 | 97.950 |
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
冻结全部卷积层权值 | 94.605 | 94.925 | 93.875 | 94.397 |
微调部分卷积层权值 | 94.605 | 95.175 | 93.925 | 94.545 |
Tab. 7 Performance comparison of two methods in literature [11] on test set
方法 | accuracy | macro-P | macro-R | macro-F1 |
---|---|---|---|---|
冻结全部卷积层权值 | 94.605 | 94.925 | 93.875 | 94.397 |
微调部分卷积层权值 | 94.605 | 95.175 | 93.925 | 94.545 |
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