A Boundary-Guided Feature Modulation Network for Weld Radiographic Defect Segmentation

Accurate pixel-level segmentation of weld defects in radiographic images is essential for automated non-destructive testing (NDT) and quantitative weld-quality assessment. However, this task remains challenging because weld defects often exhibit severe foreground–background imbalance, ambiguous boundaries, weak grayscale contrast, and defect-like weld background structures, which can lead to boundary over-expansion and false-positive predictions. To address these issues, this paper proposes a boundary-guided feature modulation framework with false-positive suppression for weld radiographic defect segmentation. The method constructs boundary bands from training annotations and uses them only as label-derived training-time regularizers for attention-driven feature modulation and region-aware optimization; during validation and testing, no ground-truth mask, boundary band, or predicted boundary map is provided to the model. Multi-scale feature fusion is used to recover weak defect responses, boundary-guided dual attention enhances boundary-sensitive feature representation, and a false-positive suppression loss penalizes foreground leakage above a tolerated confidence margin in stable non-boundary background regions. Experiments on a real-world pipeline weld radiographic dataset containing 10,079 images show that the proposed method achieves a Dice score of 0.810±0.003, a Precision of 0.809±0.004, and a Surface Dice at 3 pixels of 0.394±0.008, outperforming representative CNN-based and Transformer-based segmentation baselines. Ablation studies, qualitative visualization, and distance-based false-positive analysis further demonstrate that the proposed framework improves contour reliability and reduces background false positives.