摘要
聚乙烯(Polyethylene,PE)燃气管道的电熔焊接状况可通过相控阵系统拍摄的超声波图像获得,但是否存在焊接缺陷目前都是由专业人员人工识别每张图片中的特征线、电阻丝、底面回波线等与缺陷相关的信息,进而确定缺陷类别和等级,这种方法费时、费力且容易发生漏检和错检现象。本文针对PE管道电熔焊接缺陷的识别问题提出了一种基于图像处理技术的焊接缺陷自动识别方法,同时判断超声图像中存在缺陷的类别和等级。该方法由4个步骤组成:(1)通过数据增强技术扩充已有图片数量,构建数据集;(2)训练图像语义分割模型,对图像进行语义分割;(3)采用数学形态学方法去除分割结果的噪声,并通过连通域分析法得到缺陷相关信息;(4)依据焊接标准及缺陷相关信息识别缺陷类别及等级。实验结果表明,本文提出的缺陷识别方法在准确率、召回率以及运行时间方面均达到了工业应用要求。
The electrofusion welding status of the polyethylene(PE)gas pipeline can be obtained from the ultrasonic pictures taken by the phased array system.However,whether there are welding defects or not is judged by professionals manually identifying the information related to defects such as feature line,resistance wire,and bottom echo line in each picture,so as to determine the defect category and grade.This method is time-consuming,laborious,and prone to missed detections and false detections.Aiming at the identification of electrofusion welding defects in PE pipelines,this paper proposes an automatic identification method of welding defects based on image processing technology,which can judge the categories and grade of defects in ultrasound images at the same time.The method consists of four steps:(1)expanding the number of existing pictures through data enhancement technology to build a data set;(2)training the image semantic segmentation model to segment the image semantically;(3)using mathematical morphology to remove the noise of the segmentation result,and obtaining defect-related information via the connected domain analysis;(4)identifying the defect category and grade according to the welding standard and defect-related information.Finally,the experimental results show that the proposed defect recognition method can meet the requirements of industrial applications in terms of accuracy,recall and running time.
作者
胡剑峰
周家乐
王慧锋
张俊
HU Jianfeng;ZHOU Jiale;WANG Huifeng;ZHANG Jun(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China;Shanghai Shhydrau Electromechanical Co.Ltd,Shanghai 201800,China)
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2022年第4期526-533,共8页
Journal of East China University of Science and Technology
基金
国家重点研发计划(2018YFC1803306)
青年科学基金项目(61906068)。
关键词
电熔焊接缺陷识别
图像语义分割
数学形态学
连通域分析
焊接标准
electrofusion welding defect recognition
image semantic segmentation
mathematical morphology
connected domain analysis
welding standard
