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基于卷积神经网络的短切毡缺陷分类

Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network

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摘要

基于卷积神经网络,提出了短切毡缺陷分类的方法。通过旋转、平移和翻转对数据集进行扩充,解决了小数据样本在深度卷积神经网络中的过拟合问题;利用迁移学习的思想加速网络收敛,提高了网络的泛化能力;对比了不同网络结构并选择较好的网络进行数据集验证。结果表明,所提方法能够实现短切毡缺陷的有效分类,准确率为93%。

Abstract

In this study, a classification method of chopped strand mat defects based on convolutional neural network is proposed. In the proposed method, the rotation, translation, and inversion are employed to expand the dataset for solving the overfitting problem caused by the small data samples in the deep convolutional neural networks. Transfer learning is employed to improve the convergence speed and generalization ability of the network. Further, the different network structures are compared, and the most optimal network structure is used to verify the database. The experimental results demonstrate that the proposed method can effectively classify the chopped strand mat defects with an accuracy rate of 93%.

Newport宣传-MKS新实验室计划
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中图分类号:TP391.4

DOI:10.3788/lop56.101009

所属栏目:图像处理

基金项目:国家自然科学基金(61301276)、陕西省重点研发计划(2017GY-003)、陕西省高校科协青年人才托举计划项目(20180115)、陕西省教育厅科研计划项目(18JK0338)

收稿日期:2018-10-29

修改稿日期:2018-12-07

网络出版日期:2018-12-14

作者单位    点击查看

卓东:西安工程大学电子信息学院, 陕西 西安 710048
景军锋:西安工程大学电子信息学院, 陕西 西安 710048
张缓缓:西安工程大学电子信息学院, 陕西 西安 710048
苏泽斌:西安工程大学电子信息学院, 陕西 西安 710048

联系人作者:景军锋(jingjunfeng0718@sina.com)

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引用该论文

Zhuo Dong,Jing Junfeng,Zhang Huanhuan,Su Zebin. Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101009

卓东,景军锋,张缓缓,苏泽斌. 基于卷积神经网络的短切毡缺陷分类[J]. 激光与光电子学进展, 2019, 56(10): 101009

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