脉冲涡流红外热成像缺陷特征提取方法

Defect Feature Extraction in Eddy Current Pulsed Thermography

基于脉冲涡流红外热成像技术的缺陷特征提取与分析是无损检测领域的研究热点之一。该文提出一种新的脉冲涡流红外热图像特征提取算法并用于强化缺陷信息。该算法主要包括基于熵梯度的显著热图像选择、局部稀疏图像分离以及局部稀疏图像融合3个部分。对比于常用的两种脉冲涡流红外热成像数据特征提取算法——独立成分分析算法和鲁棒主成分分析算法,实验结果表明,该算法可以更好地强化有意义的缺陷信息并抑制包含噪声的背景区域。

In non-destructive evaluation area,defect feature extraction and analysis based on eddy current pulsed thermography (ECPT) technique is a research focus.In this paper,a novel defect feature extraction approach is proposed to highlight the defect information in ECPT.The proposed approach includes entropy-based image selection,local (element-wise) sparse decomposition and image fusion.Comparing with other two common feature extraction algorithms,independent component analysis and robust principal component analysis,the proposed algorithm can extract more defect features and suppress background.