The service life of internal combustion engines is significantly influenced by surface defects in cylinder liners. To address the limitations of traditional detection methods, we propose an enhanced YOLOv8 model with Swin Transformer as the backbone network. This approach leverages Swin Transformer’s multi-head self-attention mechanism for improved feature extraction of defects spanning various scales. Integrated with the YOLOv8 detection head, our model achieves a mean average precision of 85.1% on our dataset, outperforming baseline methods by 1.4%. The model’s effectiveness is further demonstrated on a steel-surface defect dataset, indicating its broad applicability in industrial surface defect detection. Our work highlights the potential of combining Swin Transformer and YOLOv8 for accurate and efficient defect detection.