ABSTRACT

The paper presents the results of research on the data processing algorithm used to detect material defects using active thermography. The algorithm allows the analysis of thermogram sequences in both time and image domain. In the first stage of the algorithm operation, mathematical morphology or filtered contrast methods are used to remove the uneven heating from the sample, as well as to segment and detect defects using local and global thresholding methods. In the next stage, it is possible to determine the number of defects as well as automatically estimate their depth and characteristics (insulator/conductor) in relation to the background material (material without defect). The presented algorithm was tested on two material samples, i.e. PMMA and Expanded PVC, for two phases of the thermal process, i.e. heating and cooling. The study found that the best defect detection and characterisation results are obtained when processing thermographic data from the cooling phase in combination with a Top Hat morphological transformation, local thresholding (for defect detection), and relative incremental filtered contrast (for defect size estimation).

摘要

本文介绍了使用主动热成像检测材料缺陷的数据处理算法的研究结果。该算法允许在时间和图像域中分析热图序列。在算法操作的第一阶段，使用数学形态学或滤波对比方法去除样品的不均匀加热，以及使用局部和全局阈值方法对缺陷进行分割和检测。在下一阶段，可以确定缺陷的数量，并自动估计它们相对于背景材料（无缺陷的材料）的深度和特性（绝缘体/导体）。所提出的算法在两种材料样品上进行了测试，即 PMMA 和膨胀 PVC，用于热过程的两个阶段，即加热和冷却。