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 and depth regression based on 1D CNN

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。

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(1 D-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 1 D-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.