With the advancement of electromagnetic induction thermography and imaging technology in non-destructive testing field, this system has significantly benefitted modern industries in fast and contactless defects detection. However, due to the limitations of front-end hardware experimental equipment and the complicated test pieces, these have brought forth new challenges to the detection process. Making use of the spatio-temporal video data captured by the thermal imaging device and linking it with advanced video processing algorithm to defects detection has become a necessary alternative way to solve these detection challenges. The extremely weak and sparse defect signal is buried in complex background with the presence of strong noise in the real experimental scene has prevented progress to be made in defects detection. In this paper, we propose a novel hierarchical low-rank and sparse tensor decomposition method to mine anomalous patterns in the induction thermography stream for defects detection. The proposed algorithm offers advantages not only in suppressing the interference of strong background and sharpens the visual features of defects, but also overcoming the problems of over- and under-sparseness suffered by similar state-of-the-art algorithms. Real-time natural defect detection experiments have been conducted to verify that the proposed algorithm is more efficient and accurate than existing algorithms in terms of visual presentations and evaluation criteria. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.

中文翻译：

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基于电磁热成像系统的分层低阶稀疏张量微缺陷分解

随着电磁感应热成像和成像技术在无损检测领域的进步，该系统在快速和非接触式缺陷检测方面显着受益于现代工业。然而，由于前端硬件实验设备的限制和复杂的测试件，给检测过程带来了新的挑战。利用热成像设备捕获的时空视频数据并将其与先进的视频处理算法相结合进行缺陷检测已成为解决这些检测挑战的必要替代方法。在真实的实验场景中，极微弱稀疏的缺陷信号隐藏在复杂的背景中，伴随着强噪声的存在，阻碍了缺陷检测的进展。在本文中，我们提出了一种新的分层低秩和稀疏张量分解方法来挖掘感应热成像流中的异常模式以进行缺陷检测。所提出的算法不仅在抑制强背景干扰和锐化缺陷的视觉特征方面具有优势，而且还克服了同类最新算法存在的过稀疏和欠稀疏问题。已进行实时自然缺陷检测实验，以验证所提出的算法在视觉呈现和评估标准方面比现有算法更有效和准确。本文是主题问题“高级电磁无损评估与智能监测”的一部分。