Enhancing Inductive IR Thermography by Using FFT-Equalization, Motion Tracking Detection and VDSR Super-resolution Processing

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Among non-destructive inspection techniques, infrared thermography stands out as a promising technology that enables real-time inspection of large areas without the need for physical contact. In this study, we employed the dynamic induction thermography method, which is one of the active infrared thermography techniques, to detect defects on the back side of the S275 material specimen. This technique involves creating relative movement between the IR camera and the specimen. We acquired sequence images at different moving speeds using the induction thermography technique, and then used the FFT with Gaussian filtering to solve for non-uniform heat sources. To further enhance the resolution, we applied the VDSR technique, which is based on deep neural networks. The effectiveness of this approach was validated both qualitatively and quantitatively. Finally, we utilized the MOT algorithm to automatically detect defects in the image with the highest thermal contrast, which was captured at a speed of 15 mm/s. In this study, we demonstrate the effectiveness of thermal equalization using the GF-based FFT algorithm, as well as the superresolution conversion achieved through the VDSR-based deep neural network. Additionally, we present a mechanism for automated slit detection using the MOT algorithm.