Detection of Weld Defects in Power Components Using the Attention-Enhanced YOLOv8 Model

In transformer power equipment, major welding defects often cause oil leakage due to flaws such as porosity, spatter, undercuts, and overlaps, as well as cracks and leakage resulting from frequent electromagnetic vibrations. These issues typically arise from reduced weld quality, component fatigue, thermal overload, and long-term operational conditions, ultimately leading to shorter equipment lifespan and serious safety risks. To address these challenges, in this paper, we propose an improved YOLOv8 model with a Convolutional Block Attention Module(CBAM) to detect and resolve multiple types of small defects on weld beads. The CBAM module is integrated into the neck part of the YOLOv8 network to enhance target features from both channel and spatial dimensions. The proposed YOLOv8-CBAM model demonstrated the most balanced performance across all metrics, reaching the highest precision (75.2%) and recall (77.6%). Although its mAP@50 (81.0%) was slightly lower than YOLOv5, it still outperformed YOLOv10 and YOLOv11, and its competitive mAP@50-95 (49.0%) confirmed its robustness in detecting defects of varying sizes.