Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.

This study evaluates the effectiveness of thermographic signal reconstruction using polynomial approximations for preprocessing thermographic images obtained through Pulsed Thermography in the segmentation of defects in CFRP laminates. Neural networks were trained with both processed and unprocessed images and evaluated using IoU and F1-Score metrics. The preprocessing significantly increased defects' signal-to-noise ratio and detection accuracy, achieving an IoU of 92% and an F1-Score of 98%. The technique proved effective in enhancing defect segmentation and definition, particularly in scenarios with low signal-to-noise ratio.