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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).

Investigation on dual-domain data processing algorithm used in thermal non-destructive evaluation