Swin transformer network leveraging multi-dimensional features for defect depth prediction

This study introduces a novel method for accurately predicting defect depth in pulsed infrared thermography. The core innovation of this study lies in the utilization of multi-dimensional features to enhance the accuracy of depth prediction. By integrating temperature, temperature change rate, and time-frequency spectrum into a comprehensive feature set, we aim to capture a more detailed understanding of defect characteristics, thereby facilitating more precise predictions. Additionally, we employ the Hilbert encoding method to obtain twodimensional matrices and utilize mask-based augmentation to generate synthetic defect data matrices. Subsequently, the generated matrices are fed input into a Swin transformer network configured with shifted windows and multi-head self-attention. In comparison to existing high-performing architectures, our method leveraging multi-dimensional features, demonstrates superior performance, especially in defect depth prediction for nonplanar samples. This work paves the way for more accurate and efficient defect depth prediction in various infrared thermography applications.