标题： 基于PCA引导的自动编码器的主动红外热成像结构降维方法 显示英文标题

标题： PCA-Guided Autoencoding for Structured Dimensionality Reduction in Active Infrared Thermography

摘要： 主动红外热成像（AIRT）是一种广泛采用的无损检测（NDT）技术，用于检测工业部件中的地下异常。 由于AIRT数据的高维性，当前方法使用非线性自编码器（AEs）进行降维。 然而，AIRT AEs学习到的潜在空间缺乏结构，限制了其在后续缺陷表征任务中的有效性。 为解决这一限制，本文提出了一种主成分分析引导（PCA引导）的自动编码框架，用于结构化降维，以捕捉热成像信号中的复杂非线性特征，同时强制潜在空间具有结构。 引入了一种新的损失函数，即PCA蒸馏损失，以指导AIRT AEs将潜在表示与结构化的PCA组件对齐，同时捕捉热成像信号中的复杂非线性模式。 为了评估学习到的结构化潜在空间的实用性，我们提出了一种基于神经网络的评估指标，以评估其适用于缺陷表征的适宜性。 实验结果表明，在对比度、信噪比（SNR）和基于神经网络的指标方面，所提出的PCA引导的AE在PVC、CFRP和PLA样品上优于最先进的降维方法。 显示英文摘要

摘要： Active Infrared thermography (AIRT) is a widely adopted non-destructive testing (NDT) technique for detecting subsurface anomalies in industrial components. Due to the high dimensionality of AIRT data, current approaches employ non-linear autoencoders (AEs) for dimensionality reduction. However, the latent space learned by AIRT AEs lacks structure, limiting their effectiveness in downstream defect characterization tasks. To address this limitation, this paper proposes a principal component analysis guided (PCA-guided) autoencoding framework for structured dimensionality reduction to capture intricate, non-linear features in thermographic signals while enforcing a structured latent space. A novel loss function, PCA distillation loss, is introduced to guide AIRT AEs to align the latent representation with structured PCA components while capturing the intricate, non-linear patterns in thermographic signals. To evaluate the utility of the learned, structured latent space, we propose a neural network-based evaluation metric that assesses its suitability for defect characterization. Experimental results show that the proposed PCA-guided AE outperforms state-of-the-art dimensionality reduction methods on PVC, CFRP, and PLA samples in terms of contrast, signal-to-noise ratio (SNR), and neural network-based metrics.