文献类型：期刊文献

中文题名：基于卷积神经网络的管道表面缺陷识别研究

英文题名：Research on pipe surface defect recognition based on convolutional neural network

作者：袁泽辉[1];郭慧[1];周邵萍[1]

机构：[1]华东理工大学机械与动力工程学院,上海200237

年份：2020

卷号：43

期号：17

起止页码：47

中文期刊名：现代电子技术

外文期刊名：Modern Electronics Technique

收录：CSTPCD;;北大核心:【北大核心2017】；

基金：国家自然科学基金委员会资助项目(51575185)。

语种：中文

中文关键词：缺陷识别;管道表面缺陷;机器视觉;卷积神经网络;缺陷分类;GoogleNet构造优化

外文关键词：defect recognition;pipeline surface defect;machine vision;convolutional neural network;defect classification;GoogleNet structure optimization

摘要：针对传统管道表面缺陷检测方法存在效率低、准确率不高的问题,提出一种通过机器视觉检测管道表面缺陷的方法,在采集管道表面缺陷的图像信息后通过卷积神经网络的算法分类不同的缺陷。通过加入批量归一化层,改进低层和中层卷积核的构造,优化了GoogleNet的构造,提高了卷积神经网络的泛化性和收敛性。试验结果表明,应用卷积神经网络后对管道表面缺陷的识别率较高,显著提高了管道表面缺陷识别的效率和准确率,具有较好的工程意义。
As the traditional pipeline surface defect detection method has the problems of low efficiency and low accuracy,a method based on machine vision to detect pipeline surface defects is proposed,in which the algorithm of convolutional neural network is used to classify different defects after the image information of pipeline surface defects is collected,and the low-level and middle-level convolution kernels are improved by adding a batch normalization-level to optimize the GoogleNet structure and enhance the generalization and convergence of convolutional neural networks. The test results show that the method’s recognition rate of pipeline surface defects is higher after the application of convolutional neural network,which significantly improves the recognition efficiency and accuracy of defects on the pipeline surface. Therefore,the method has good engineering significance.