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基于全卷积神经网络的焊缝特征提取

Weld Feature Extraction Based on Fully Convolutional Networks

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摘要

基于深层卷积神经网络的特征学习能力, 提出了一种基于全卷积神经网络的焊缝特征提取方法。该方法利用全卷积神经网络将包含焊缝特征信息的像素预测出来, 通过融合低层与高层特征信息来补充焊缝边缘的特征信息。研究结果表明:所提方法能在强烈弧光和烟尘干扰下准确地提取出焊缝位置, 具有抗干扰能力强、识别准确的优点。

Abstract

Based on the feature learning ability of deep convolutional neural networks, a weld feature extraction method based on fully convolutional networks is proposed. In this method, the fully convolutional networks is used to predict the pixels containing the feature information of the weld, and the edge feature information of weld is supplemented by the fusion of low-level and high-level feature information. The results show that the method can get the weld position accurately under the interference of strong arc and soot particles, and has the advantages of strong anti-interference ability and accurate recognition.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP242.2

DOI:10.3788/cjl201946.0302002

所属栏目:激光制造

基金项目:国家自然科学基金青年科学基金项目(51805370)、天津科技大学青年教师创新基金(2017LG08)

收稿日期:2018-07-20

修改稿日期:2018-10-22

网络出版日期:2018-11-22

作者单位    点击查看

张永帅:天津科技大学电子信息与自动化学院, 天津 300222
杨国威:天津科技大学电子信息与自动化学院, 天津 300222
王琦琦:天津科技大学电子信息与自动化学院, 天津 300222
马雷:天津科技大学电子信息与自动化学院, 天津 300222
王以忠:天津科技大学电子信息与自动化学院, 天津 300222

联系人作者:杨国威(yangguowei@tust.edu.cn)

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引用该论文

Zhang Yongshuai,Yang Guowei,Wang Qiqi,Ma Lei,Wang Yizhong. Weld Feature Extraction Based on Fully Convolutional Networks[J]. Chinese Journal of Lasers, 2019, 46(3): 0302002

张永帅,杨国威,王琦琦,马雷,王以忠. 基于全卷积神经网络的焊缝特征提取[J]. 中国激光, 2019, 46(3): 0302002

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