AI-driven pulse thermography (PT) has become a crucial tool in non-destructive testing (NDT), enabling automatic detection of hidden anomalies in various industrial components. Current state-of-the-art PT segmentation and depth estimation networks rely on thermal features extracted from Principal Component Thermography (PCT) or Thermographic Signal Reconstruction (TSR) representations. However, relying on PCT and TSR independently constrains the performance of PT inspection models, as these representations possess complementary semantic features. To address this limitation, this work proposes PT-Fusion, a novel feature fusion network for enhanced analysis of subsurface defects in PT setups. PT-Fusion introduces novel fusion modules, Adaptive Weighing Fusion Gate (AWFG) and Gating Enhanced Decoding Block (GEDB), to adaptively fuse thermal features extracted from PCT and TSR representations. A novel data augmentation technique is also proposed based on random data sampling from thermographic sequences to address the scarcity of PT datasets. PT-Fusion is benchmarked against state-of-the-art PT inspection models, including U-Net, attention U-Net, 3D-CNN, TransUNet, and Swin-UNet on the Université Laval IRT-PVC dataset. The results demonstrate that PT-Fusion outperforms U-Net, attention U-Net, and 3D-CNN architectures in defect segmentation and depth estimation accuracies with a margin of 10%. Compared to TransUNet and Swin-UNet, the results show that PT-Fusion's performance is on par with the aforementioned models, with fewer parameters. Video Link.
Salah et al. (Tue,) studied this question.