DyHRMADet-enabled terahertz detection and identification for composite defects

For defect detection in composite materials, Terahertz time-domain non-destructive testing (THz NDT) remains hindered by the inherently low contrast of THz images, noise interference, and the extremely small size of minor defects compared to the background. Here we propose an autonomous defect detection model leveraging the Dynamic High-Resolution Multi-Level Attention Detection Network (DyHRMADet), to enable rapid and precise detection of minor and hidden defects in composite materials. DyHRMADet, consisting of crafted modules, employs an attention-driven multi-scale framework that simultaneously captures fine-grained spatial details and high-level semantic information from THz images, and effectively integrates features across multiple resolutions, delivering a streamlined yet highly accurate defect detector. Comparative performance evaluations indicate that DyHRMADet realises highest mean average precision (mAP) across IoU thresholds (mAP@0.5:0.95 = 0.732, mAP@0.5 = 0.993, mAP@0.75 = 0.846), while processing each image in 0.287 ms with a computational cost of 9.535 GFLOPs and 24.975 MB parameters. It confirms that the proposed DyHRMADet effectively overcomes the limitations of the low contrast and noise inherent in THz images, and realises the accurate minor defect detection. Overall, the proposed approach sets a novel benchmark of THz NDT for defect characterisation in industrial settings, addressing the critical demand for high-precision defect detection in composite materials.