YOLOv8-FE: a multi-scale feature enhancement and attention-guided fusion model for steel surface defect detection

Abstract Steel surface defects can significantly compromise the safety, durability, and cost efficiency of steel structures; however, vision-based automated inspection remains challenging due to complex backgrounds, scale variation, and low defect-background contrast. To address these challenges, we propose YOLOv8-FE (you only look once v8-feature enhancement), an enhanced detector built on YOLOv8. The proposed model comprises three modules: multi-scale feature enhancement, multi-scale feature fusion, and convolution-attention aggregation. Together, these modules enhance defect representation and contextual modeling. Experiments on the NEU-DET and GC10-DET datasets demonstrate consistent performance gains. On NEU-DET, YOLOv8-FE achieves 78.0% mAP50, 57.3% recall, and 87.2% precision, corresponding to absolute gains of 2.8%, 0.7%, and 4.4%, respectively. On GC10-DET, it achieves 72.5% mAP50, 55.4% recall, and 88.1% precision, with absolute gains of 4.4%, 2.9%, and 1.5%, respectively. These results indicate that YOLOv8-FE enhances the accuracy and robustness of steel surface defect detection, providing an effective solution for related industrial visual inspection tasks.