Abstract
The reliable detection of spot-weld defects in automotive structural components is challenged by large variations in defect scale, severe background interference and limited detection accuracy. Here, we propose YOLOv8-RFA-iEMA-RH, an improved YOLOv8n-based detector for spot-weld defects. A receptive field attention convolution module (RFACM) is introduced into the backbone to strengthen local texture representation through multi-receptive-field feature modelling. An improved Efficient Multi-scale Attention module (iEMA) is incorporated into the neck to enhance global context modelling and suppress background interference. In addition, a structurally re-parameterized RepHead is integrated into the detection head to enhance feature learning during training while maintaining a simplified single-branch structure for inference. On the self-built spot-weld defect dataset, the proposed model achieves 92.7% Recall, 91.2% F1, 97.9% mAP@0.5 and 71.9% mAP@0.5:0.95, improving on the YOLOv8n baseline by 2.3, 1.3, 2.5 and 3.1 percentage points, respectively. Cross-dataset evaluation on NEU-DET further yields 78.8% mAP@0.5 and 48.7% mAP@0.5:0.95. These results demonstrate improved detection accuracy and cross-dataset adaptability; actual inference speed and memory consumption require further validation on specific deployment hardware.