[1]陈立潮,闫耀东,张睿,等.融合迁移学习的AlexNet神经网络不锈钢焊缝缺陷分类[J].智能系统学报,2021,16(3):537-543.[doi:10.11992/tis.202005013]
 CHEN Lichao,YAN Yaodong,ZHANG Rui,et al.Welding defect classification of stainless steel based on AlexNet neural network combined with transfer learning[J].CAAI Transactions on Intelligent Systems,2021,16(3):537-543.[doi:10.11992/tis.202005013]
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融合迁移学习的AlexNet神经网络不锈钢焊缝缺陷分类(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年3期
页码:
537-543
栏目:
学术论文—人工智能基础
出版日期:
2021-05-05

文章信息/Info

Title:
Welding defect classification of stainless steel based on AlexNet neural network combined with transfer learning
作者:
陈立潮1 闫耀东1 张睿1 傅留虎2 曹建芳13
1. 太原科技大学 计算机科学与技术学院,山西 太原 030024;
2. 山西省机电设计研究院 机械产品质量监督检验站,山西 太原 030009;
3. 忻州师范学院 计算机科学与技术系,山西 忻州 034000
Author(s):
CHEN Lichao1 YAN Yaodong1 ZHANG Rui1 FU Liuhu2 CAO Jianfang13
1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;
2. Mechanical Product Quality Supervision and Inspection Station, Shanxi Mechanical and Electrical Design & Research Institute, Taiyuan 030009, China;
3. Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou 034000, China
关键词:
不锈钢焊缝缺陷分类卷积神经网络图像预处理AlexNet模型迁移学习数据增强焊缝数据集深度学习
Keywords:
classification of weld defects in stainless steelconvolutional neural networkimage preprocessingAlexNet modelthe migration studydata enhancementweld data setdeep learning
分类号:
TP391.4
DOI:
10.11992/tis.202005013
摘要:
针对不锈钢焊缝缺陷特征提取存在主观单一性和客观不充分性等问题,提出一种融合迁移学习的AlexNet卷积神经网络模型,用于不锈钢焊缝缺陷的自动分类。首先,由于不锈钢焊缝缺陷数据较为缺乏,通过采用迁移学习对网络前3层冻结,减少网络对输入数据量的要求;对后2层卷积层提取的特征信息批量归一化(batch normalization, BN),以加快网络的收敛速度;并使用带泄露线性整流(leaky rectified linear unit, LeakyReLU)函数对抑制神经元进行激活,从而提高模型的鲁棒性和特征提取能力。结果表明,该模型最终达到了95.12%的准确率, 相比原结构识别精度提高了9.8%。验证了改进后方法能够对裂纹、气孔、夹渣、未熔合和未焊透5类不锈钢焊缝缺陷实现高精度分类。相比现有方法,其识别面更广,精度更高,具有一定的工程实践意义。
Abstract:
In order to solve the problems of subjectivity and objectivity in feature extraction of stainless steel weld defects, an AlexNet convolutional neural network model based on transfer learning is proposed for automatic classification of stainless steel weld defects. First, due to the lack of stainless steel weld defect data, the first three layers of the network are frozen by transfer learning, which reduces the requirement of the network on the input data. In order to speed up the convergence of the network, the batch normalization (BN) of the feature information extracted from the two latter layers of convolution is carried out. The LeakyReLU function is used to activate the features in the negative interval so as to improve the robustness of the model and the ability of feature extraction. The results show that the accuracy of the model is 95.12%, and the recognition accuracy is 9.8% higher than that of the original structure. It has been verified that the improved method can classify five kinds of stainless steel weld defects such as crack, blowhole, slag inclusion, incomplete fusion, and incomplete penetration with high precision. Compared to the existing methods, this method has a wider recognition area, higher accuracy, and certain engineering significance.

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备注/Memo

备注/Memo:
收稿日期:2020-05-10。
基金项目:先进控制与装备智能化山西省重点实验室开放课题(ACEI202002);山西省高等学校科技创新项目(2019L0653);山西省应用基础研究项目(201801D221179)
作者简介:陈立潮,教授,博士,主要研究方向为人工智能、图像信息处理。主持山西省自然科学基金等项目12项。发表学术论文180余篇;闫耀东,硕士研究生,主要研究方向为图像信息处理;张睿,副教授,博士,主要研究方向为智能信息处理。主持山西省应用基础研究等项目5项。发表学术论文10余篇
通讯作者:张睿.E-mail:zhangrui@tyust.edu.cn
更新日期/Last Update: 2021-06-25