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基于直觉模糊C均值聚类的红外图像缺陷检测

王禄祥 张志杰 汪权 陈昊泽

王禄祥, 张志杰, 汪权, 陈昊泽. 基于直觉模糊C均值聚类的红外图像缺陷检测[J]. 红外技术, 2022, 44(11): 1220-1227.
引用本文: 王禄祥, 张志杰, 汪权, 陈昊泽. 基于直觉模糊C均值聚类的红外图像缺陷检测[J]. 红外技术, 2022, 44(11): 1220-1227.
WANG Luxiang, ZHANG Zhijie, WANG Quan, CHEN Haoze. Infrared Image Defect Detection Based on the Algorithm of Intuitionistic Fuzzy C-Means Clustering[J]. Infrared Technology , 2022, 44(11): 1220-1227.
Citation: WANG Luxiang, ZHANG Zhijie, WANG Quan, CHEN Haoze. Infrared Image Defect Detection Based on the Algorithm of Intuitionistic Fuzzy C-Means Clustering[J]. Infrared Technology , 2022, 44(11): 1220-1227.

基于直觉模糊C均值聚类的红外图像缺陷检测

基金项目: 

国家自然科学基金 52275550

详细信息
    作者简介:

    王禄祥(1995-),男,河南上蔡人,硕士研究生,主要从事无损检测、信号处理和图像处理方面的研究。E-mail:wanglx16112@163.com

    通讯作者:

    张志杰(1965-),男,山西五台人,教授,博士生导师,主要从事动态测试理论与信号处理、动态误差及不确定度等方面的研究。E-mail:zhangzhijie@nuc.edu.cn

  • 中图分类号: TP391

Infrared Image Defect Detection Based on the Algorithm of Intuitionistic Fuzzy C-Means Clustering

  • 摘要: 红外热成像技术常被用来检测碳纤维增强复合材料的内部缺陷,但常用的光学热源加热效率低,需要近距离加热试件。激光具有能量集中、衰减小的优点,其作为加热源有助于实现远距离检测。本文介绍了线激光扫描红外热成像无损检测技术,并对加热过程中材料内部热传导进行了分析。其次,针对红外图像均匀性差、对比度弱,不利于缺陷特征提取的问题,本文引入基于直觉模糊C均值聚类算法的图像分割方法来提取缺陷边缘,与K-Means聚类方法相比,该方法可以提升缺陷模糊边缘的识别和检测能力,保留更多图像的细节信息,有助于准确提取缺陷边缘特征。
  • 图  1  线激光扫描检测原理示意图

    Figure  1.  Schematic diagram of linear laser scanning inspection principle

    图  2  CFRP 试件及人工缺陷

    Figure  2.  CFRP sample and artificial defects

    图  3  激光扫描热成像无损检测系统

    Figure  3.  Diagram of laser scanning thermal imaging non-destructive testing system

    图  4  3 mm圆盘形孔激励过程的序列图像

    Figure  4.  Sequential images of 3 mm disc-shaped hole in the excitation process

    图  5  缺陷红外图像

    Figure  5.  Infrared images of defects

    图  6  IFCM算法缺陷特征提取流程

    Figure  6.  Flow chart of defect feature extraction in IFCM algorithm

    图  7  IFCM缺陷特征提取结果

    Figure  7.  Defect feature extraction result in IFCM algorithm

    图  8  K-Means算法聚类结果

    Figure  8.  Clustering results of K-Means algorithm

    表  1  T800型CFRP热特性

    Table  1.   Thermal properties of T800 CFRP

    Properties Parameters
    Density ρ/(kg/m3) 1536
    Specific heat capacity c/(J/(kg·K)) 865
    Thermal conductivity k/(W/(m·K)) 4.2
    (Along fiber direction)
    0.56
    (Perpendicular to fiber direction)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-20
  • 修回日期:  2022-07-20
  • 刊出日期:  2022-11-20

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