Semi-supervised defect recognition method based on contractive convolutional autoencoder

被引：0
|
作者：
Gao Y. [1 ]
Li X. [1 ]
Gao L. [1 ]
机构：
[1] School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2021年 / 49卷 / 07期
关键词：
Convolutional autoencoder; Deep learning; Defect recognition; Quality control; Semi-supervised learning;
D O I：
10.13245/j.hust.210716
中图分类号：
学科分类号：
摘要：
A defect recognition method based on semi-supervised convolutional contractive autoencoder is proposed. The effective defect information obtained from unlabeled data was combined with a small number of labeled samples to achieve a higher defect recognition effect, which effectively solves the problem that the traditional defect recognition method based on convolutional neural network relies on a large number of labeled samples. The experimental results show that the proposed method has a high recognition accuracy and can achieve a good recognition effect under a small number of labeled samples. Compared with other methods, the accuracy rate is increased by 4.93%~62.96%, which can effectively reduce the cost of sample labeling, speed up the model deployment, and ensure the smooth progress of quality testing and production planning. © 2021 Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：92 / 96
页数：4
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