This paper uses infrared thermal wave nondestructive testing experiments to simulate corrosion defects in aluminum alloy materials. The experimental results are processed by the least squares method to remove the fitting background and the pulse phase method, thereby improving the detection ability of defects. The processing effect is quantitatively evaluated using statistical image evaluation standards. The results show that infrared thermal wave detection technology can make fast, effective and intuitive detection of defects; the fitting background removal method can effectively reduce the adverse effects of uneven heating on defect recognition; the pulse phase method has a certain inhibitory effect on random noise in the spatial domain and has a certain recognition ability when locating the depth of defects. Keywords: image processing; nondestructive testing; aluminum alloy plate; defect recognition Infrared thermal wave nondestructive testing has the advantages of large detection area, high detection efficiency, one-way non-contact and the ability to directly measure depth and thickness. Therefore, it is increasingly widely used in material defect or structure detection [1]. However, due to the characteristics of the detector itself and the influence of the external environment, infrared thermal images generally have disadvantages such as poor contrast between the target and the background, blurred image edges and large noise. Therefore, it is of great significance to explore effective thermal image processing methods for defect recognition.
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