The existing infrared thermal imaging detection methods usually process the whole video stream data collected by a thermal camera, which involve large amounts of data and have a negative effect on the efficiency of defect detection. In this paper, we propose an infrared thermal imaging detection method which considers the spatial correlation of the adjacent images in the video stream data. By extracting the edge information and analyzing the correlation between two adjacent frames, the defect area and the non-defect area show different correlation coefficients, and only part of the video data is required for defect detection. Furthermore, the fusion method is introduced to enhance the image quality. The experiment results demonstrate that the proposed method can not only reflect the change of heat in the defect area during the heating process but also reduce computation time involved in the subsequent processing.
An enhanced magneto-optic (MO) imaging system is presented to detect the invisible and buried subsurface flaws in
metallic specimens. The choice of the MO thin films, the design of the magnetic excitation device and the development
of the image processing approaches are presented in this paper and the quality improvement in MO imaging has been
demonstrated due to the proposed methods. Experimental results have been provided and verify the reliability and
accuracy of the enhanced MO imaging system.
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