牟欣颖,何赟泽,王洪金,邓堡元,杨 渊,周 可,杨瑞珍.基于一维卷积神经网络的联动扫描热成像缺陷
自动识别与深度回归[J].电子测量与仪器学报,2021,35(4):211-217 |
基于一维卷积神经网络的联动扫描热成像缺陷
自动识别与深度回归 |
Joint scanning thermography defect automatic classifier anddepth regression based on 1D CNN |
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DOI: |
中文关键词: 红外热成像 机器视觉 深度学习 缺陷 |
英文关键词:infrared thermography machine vision deep learning defect |
基金项目:国防科技大学装备综合保障技术重点实验室基金(6142003200205)、国家自然科学基金 青年科学基金(Z20190142984)、湖南省科技创新计划项目科技人才专项(2018RS3039)、长沙市杰出创新青年培养计划(kq1802023)、长沙市科技计划项目(CSKJ2020 19)资助 |
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中文摘要: |
联动扫描热成像(joint scanning thermography, JST)可以用于检测大面积对象的缺陷,但原始热图像缺陷信息模糊且无
法实现缺陷定量。 针对联动扫描热成像重构后的图像序列,提出了一种基于一维卷积神经网络( one-dimensional convolutional
neural network,1D-CNN)的缺陷识别和定量方法,以图像序列中像素点对应的一维温度时间序列为网络输入,将缺陷深度作为
输出,实现了碳纤维复合材料板中缺陷的自动检测和深度定量。 实验结果显示,基于 1D-CNN 的检测方法准确实现了对缺陷自
动检测,其对训练集数据的预测准确率最高可达 98. 8%,测试集准确率在 70%左右,相比传统处理方法取得了更好的效果。 |
英文摘要: |
Joint scanning thermography(JST) can detect defection of large-area materials. The defection of raw images is inaccurate and
the quantitative analysis is hard to achieve. According to the characteristics of images from the reconstruction of joint scanning
thermography, a method based on an one-dimensional convolutional neural network ( 1D-CNN) is proposed to detect and quantitate
defects. The one-dimensional temperature time series corresponding to the pixels of the pulse image sequences is applied as inputs for the
network. This method could achieve defect detection automatically and defect quantification for carbon fiber reinforced polymer. As the
result indicated, the 1D-CNN based method could detect defection automatically and accurately. It has a 98. 8% accuracy in defect
classifying of training set and an about 70% accuracy in defect classifying of training set. The result is better than traditional method. |
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