Few-Shot Surface Defect Detection in Sinusoidal Wobble Laser Welds Using StyleGAN2-AFMS Augmentation and YOLO11n-WAFE Detector

In the manufacturing of high-reliability components, sinusoidal wobble laser welding has gained preference due to its excellent performance. However, surface defect inspection for such welds is challenged by large variations in defect scales, the coexistence of multiple defects, and scarce samples, which collectively limit existing detection methods. To address these issues, this paper proposes a lightweight detection framework that integrates a generative adversarial network with an improved YOLO architecture. First, a frequency-domain-enhanced StyleGAN2-AFMS model is constructed to effectively augment high-quality defect samples. Second, a YOLO11n-WAFE detector is designed, which incorporates an ADownECA downsampling module to enhance the capability of capturing subtle defects and an Edge-Aware Semantic–Detail Fusion module to improve discriminative robustness under multi-defect conditions. To validate the approach, an industrial-level Sinusoidal Wobble Laser Weld Defect Dataset is built. Experiments reveal that the proposed framework boosts mAP@0.5 to 94.2% (an 8% improvement over the baseline) and mAP@0.5:0.95 to 77.4%, with an F1-score of 89.5%, while maintaining lightweight (2.15 M parameters) and fast (656 FPS) characteristics. This study provides a high-precision and efficient solution for few-shot industrial defect inspection.