A physics-guided memory-enhanced semi-supervised approach for detecting weld defects in radiographic images

Deep learning-based weld defect detection in industrial X-ray imaging is often constrained by scarce annotated data, high labeling costs, and poor generalization to unseen scenarios. To address these challenges, this paper proposes a physics-guided, memory-enhanced semi-supervised defect detection model (PMSDM). The model constructs normal pattern representations from easily accessible defect-free images via a localization memory module, enhances anomaly learning through artificial defects generated based on X-ray imaging principles to better define classification boundaries, and achieves fine-grained classification using a small number of labeled defect samples through a classification memory module. Localization and segmentation of tiny defects are performed via difference-based comparison with the memory bank, further refined by a spatial attention module that fuses multi-scale feature information. Experiments on the self-built dataset and the publicly available GDXray dataset show that PMSDM consistently outperforms traditional and deep learning baselines in terms of Dice , IoU , Accuracy , and mAP , while maintaining strong generalization under domain shifts. PMSDM offers an efficient and scalable solution for weld defect detection in data-scarce industrial environments.