计算机工程与应用2018，Vol.54Issue(6)：264-270,7.DOI:10.3778/j.issn.1002-8331.1609-0322
基于机器视觉方法的焊缝缺陷检测及分类算法
Effective method of weld defect detection and classification based on machine vision
摘要
Abstract
In order to effectively identify and classify weld defects of thin-walled metal canisters in industrial production, a weld defect detection and classification algorithm based on machine vision is proposed in this paper.By using the Gauss-ian mixture model,a modified background subtraction method is proposed to extract the feature areas of the weld defects. On this basis,it proposes an algorithm for weld detection and classification according to the extracted features,such as the defect areas,the defect brightness and the gray-value curves.Experimental results show that the proposed algorithms can identify and classify the thin-walled weld defects with more than 96% of accuracy rate and can meet the requirement of the real-time and continuous weld defect detection.关键词
机器视觉/焊缝缺陷检测/焊缝缺陷类型识别/混合高斯模型/背景差分法/波形检测法Key words
machine vision/weld defect detection/weld defect classification/Gaussian mixture model/background sub-traction/curve detection method分类
信息技术与安全科学引用本文复制引用
李超,孙俊..基于机器视觉方法的焊缝缺陷检测及分类算法[J].计算机工程与应用,2018,54(6):264-270,7.基金项目
国家自然科学基金(No.61672263). （No.61672263）
