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 two-dimensional 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 non-planar samples. This work paves the way for more accurate and efficient defect depth prediction in various infrared thermography applications.