首页|基于模糊模式识别的焊缝缺陷图像检测

基于模糊模式识别的焊缝缺陷图像检测

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
  • 维普
以埋弧焊管焊缝的X射线检测图像为对象,通过图像处理、特征提取和模糊识别实现了对缺陷的识别.为提高识别精度与实时性,采用主成分分析法对采集图像的像素矩阵进行了主元分析,结合模糊识别中的模糊C均值聚类算法对圆形缺陷和线形缺陷进行识别.相比于传统的通过提取缺陷的若干几何特征分类识别的方法,此方法具有算法简单、占用内存空间小、识别准确率高、实时性强等特点.最终平均识别率可达到90.93％,能够较准确地对焊缝缺陷进行分类识别.
Image Detecting of Weld Defect Based on Fuzzy Pattern Recognition
The recognition of the defects in the welding seam of submerged-arc welded pipe is finished through image processing,feature extraction and fuzzy recognition.In order to improve the accuracy and real-time performance of defect recognition,the principal component analysis (PCA) of the pixel matrix of the acquired image is carried out,and the circular defects and linear defects in the submerged-arc welding seam are recognized using fuzzy C-means clustering (FCM) algorithm.Compared with the traditional classification identification method based on the extracted geometric features of the defects,this algorithm is simpler,its occupied memory space is smaller,its recognition accuracy is higher and its real-time performance is stronger.The average defect recognition ratio of the algorithm can reach to 90.93％,and the accurate classification identification of the defects in the submerged-arc welding seam can be finished using this method.

defect recognitionwelding seam defectX-ray detectionprincipal component analysisfuzzy C-means clusteringpixel matrix

王欣、高炜欣、武晓朦、王征、李华

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西安石油大学陕西省油气井测控技术重点实验室,陕西西安710065

西安石油大学光电油气测井与检测教育部重点实验室,陕西西安710065

西安卫星测控中心工程处,陕西西安710043

缺陷识别 焊缝缺陷 X射线检测 主成分分析法 模糊C均值聚类 像素矩阵

陕西省自然科学项目陕西省教育厅重点实验室科研计划项目陕西省教育厅自然科学专项陕西省自然科学基础研究计划青年人才项目

2013JQ804914JS0792013JK10772015JQ5129

2016

10.3969/j.issn.1673-064X.2016.04.020

西安石油大学学报(自然科学版)
西安石油大学

西安石油大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.788
ISSN：1673-064X
年,卷(期)：2016.31(4)
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