摘要
为了提高钢材焊接缺陷的查准率、召回率和检测效率,提出了基于机器视觉和卷积神经网络的钢材焊接缺陷检测方法。采用灰度化处理、均值滤波和中值滤波方法对图像进行增强;基于鲁棒性主成分分析方法提取焊接缺陷特征,再通过阈值分割和形态学腐蚀-膨胀的操作对缺陷进行定位。对于焊接缺陷检测问题,通过改进的ResNet152卷积神经网络模型搭建分类器进行训练,并与传统的ResNet152、InceptionNet-v3、ResNet101、DenseNet和ResNet50分类器进行比较。试验结果表明:改进的ResNet152分类器的综合性能优于其他5种网络模型,召回率为99.05%,查准率为98.11%,平均精确率为97.54%。本方法对钢材焊接缺陷检测具有一定实践意义。
In order to improve the detection rate,recall rate and detection efficiency of steel welding defects,a steel welding defect detection method based on machine vision and convolutional neural network was proposed.Graying processing,mean filtering and median filtering methods were used to enhance the image;welding defect features were extracted based on robust principal component analysis(RPCA),and then the defects were localized by threshold segmentation and morphological corrosion-expansion operations.For the welding defect detection problem,a classifier was built by improving the traditional ResNet152 convolutional neural network model for training and it was compared with the traditional ResNet152,InceptionNet-v3,ResNet101,DenseNet and ResNet50 classifiers.The test results show that the comprehensive performance of the improved ResNet152 classifier is better than those of the other five network models,the recall rate is 99.05%,the checking accuracy rate is 98.11%,the mean average precision is 97.54%.The present method has a certain degree of practical significance for the detection of welding defects of steel.
作者
唐威
石艳
李淇
陈强
郝琪
TANG Wei;SHI Yan;LI Qi;CHEN Qiang;HAO Qi(School of Mechanical Engineering,Sichuan University of Light and Chemical Engineering,Yibin 643000,China)
出处
《热加工工艺》
北大核心
2025年第13期41-47,共7页
Hot Working Technology
基金
四川省科技计划重点研发项目(2022YFG0068)
四川省研究生创新基金项目(Y2023087)。
关键词
机器视觉
鲁棒性主成分分析
卷积神经网络
焊接缺陷
machine vision
robust principal component analysis(RPCA)
convolutional neural network
welding defects
