电子测量与仪器学报

卜迟武,刘涛,赵博,李锐.CFRP / Al 蜂窝结构缺陷红外热图特征的多结构形态学-PCNN 识别[J].电子测量与仪器学报,2021,35(9):222-228

CFRP / Al 蜂窝结构缺陷红外热图特征的多结构形态学-PCNN 识别

Recognition of defects in CFRP / Al honeycomb structure bymulti-structure morphology-PCNN

DOI：

中文关键词: CFRP / Al 蜂窝结构缺陷红外热成像主成分分析多结构形态学 PCNN 图像分割

英文关键词:CFRP / Al honeycomb structure defects infrared thermal imaging principal component analysis multi structure morphology PCNN image segmentation

基金项目:国家自然科学基金面上项目（51775175）、黑龙江省自然科学基金（LH2021E088）、黑龙江省省院合作项目（YS18A18）资助

作者	单位
卜迟武	1.哈尔滨商业大学轻工学院
刘涛	1.哈尔滨商业大学轻工学院
赵博	1.哈尔滨商业大学轻工学院
李锐	1.哈尔滨商业大学轻工学院

Author	Institution
Bu Chiwu	1.School of Light Industry, Harbin University of Commerce
Liu Tao	1.School of Light Industry, Harbin University of Commerce
Zhao Bo	1.School of Light Industry, Harbin University of Commerce
Li Rui	1.School of Light Industry, Harbin University of Commerce

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中文摘要:

CFRP / Al 蜂窝结构在制备和长期服役过程中易产生脱粘、分层、积水等缺陷,因此采用红外热波无损检测技术对其状态进行检测尤为重要。在采集 CFRP / Al 蜂窝结构缺陷红外热图像序列的过程中,存在着较大的背景噪声,容易产生对缺陷的检测效率低、对比度差等问题。为了提高缺陷检测效果,采用主成分分析算法对去除背景后的红外图像序列进行缺陷特征信息降维处理,有效滤除红外图像序列中的不均匀背景噪声。结合多结构形态学和脉冲耦合神经网络(PCNN)混合算法对缺陷区域进行图像增强和图像分割来提取缺陷区域。实验结果表明,采用上述方法,能够进一步地滤除红外图像的不均匀背景噪声, 改善缺陷区域提取效果,有效提高缺陷检出率。

英文摘要:

In the process of preparation and long-term service, CFRP / Al honeycomb structure is prone to debonding, delamination, ponding and other defects, so it is very important to detect its state by infrared thermal wave nondestructive testing technology. And In the process of collecting the infrared thermal image sequence of CFRP / Al honeycomb structure defects, there is a large background noise, which easily leads to low detection efficiency and poor contrast. In order to improve the defect detection effect, principal component analysis (PCA) algorithm is used to reduce the dimension of defect feature information of the infrared image sequence after background removal, which can effectively filter out the uneven background noise in the infrared image sequence. Combined with multistructure morphology and PCNN hybrid algorithm, the defect area is extracted by image enhancement and image segmentation. The experimental results show that the proposed method can further filter out the uneven background noise of the infrared image, improve the defect area extraction effect, and effectively improve the defect detection rate.

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