DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion

Abstract

Weld defect detection poses significant challenges including ambiguous boundaries, diverse defect shapes, and the requirement for precise localization. To address these issues, we propose DSF-YOLO, a novel framework specifically designed for pipeline weld defect detection. DSF-YOLO introduces three core innovations. The Dynamic Staged Fusion Feature Extraction (DSFFE) module dynamically fuses same-scale features from dual-backbone networks, progressively enhancing the representation of defect features and enabling the model to efficiently capture small-sized defects, blurred boundaries, and complex defect characteristics. The Dual Multi-Scale Feature Fusion (DMFF) module builds on the feature extraction capabilities of DSFFE and employs a dual fusion strategy to effectively aggregate global and local features, enhancing the representation of small targets and improving the separation of blurred boundaries. The decoupled head based on SENetv2-ResNeXt incorporates a multi-channel parallel processing strategy to further strengthen feature representation while inter-channel information interaction and global feature representation significantly improve classification and localization precision. Validated on an X-ray weld defect dataset containing 8 defect types, DSF-YOLO achieved an mAP50:95 of 74.7% surpassing YOLOv8-X by 1.1% and an mAP50 of 99.1% exceeding YOLOv8-X by 0.3%. Additionally, DSF-YOLO significantly reduces computational complexity, achieving a 75% reduction in FLOPs and a 47.5% decrease in parameters compared to YOLOv8-X. These results establish DSF-YOLO as an efficient and accurate solution addressing critical challenges in industrial weld defect detection with significant practical value.