SFW-YOLO: A lightweight multi-scale dynamic attention network for weld defect detection in steel bridge inspection

Automated detection of surface defects in steel bridge welds using computer vision is challenge due to complex weld textures, multi-scale defects, and small-size, low contrast defects. To address these challenges, this study proposes a lightweight and efficient detection algorithm , Small Feature-aware Welding YOLO (SFW-YOLO), based on the YOLOv8s model. The model improves small-size defects recognition by integrating the high-resolution P2 layer from the backbone into the neck, reducing false detection rates. It also incorporates a Dynamic Convolution Detection Head (DyHead) that utilizes scale, spatial, and task-aware attention mechanisms to optimize processing, improving defect detection accuracy in…

• NN
  National Natural Science Foundation of China

  Awards: 52478435, 52408175, 52178108, 52478128
• NS
  Natural Science Foundation of Hunan Province

  Awards: 2024JJ5033, 2024JJ6208, 2024JJ7131, 2023JJ50180
• HU
  Hunan University of Science and Technology

  Awards: S202411535070, LXBZZ2024324
• SR
  Scientific Research Foundation of Hunan Provincial Education Department

  Awards: 24A0411, 24B0534, 23A0435, 23B0575