The surface quality of steel plate products significantly impacts their performance and market competitiveness. To address the challenges of insufficient detection accuracy, frequent false positives, and severe missed detections in steel plate surface defect detection, this paper proposes an improved model, SGF-YOLOv8n, based on YOLOv8n. First, a Slim-neck structure is introduced to effectively reduce the model′s parameter count and computational complexity, thereby enhancing computational efficiency. Second, the GAM attention mechanism is integrated to strengthen the model′s perception of global features, improving its ability to detect subtle defects. Finally, the Focaler-IoU loss function is employed to further optimize the model′s localization accuracy when dealing with blurry boundaries and small defect areas. Additionally, to address the issue of limited dataset samples, data augmentation techniques were applied to expand the NEU-DET dataset, followed by extensive experiments. The experimental results demonstrate that SGF-YOLOv8n achieves an mAP50 of 81.6% on the NEU-DET dataset, representing a 3.8% improvement over the baseline model. Furthermore, in generalization experiments on the GC10-DET dataset, SGF-YOLOv8n achieved an mAP50 of 70.4%, a 6.7% increase compared to the baseline. These results indicate that the proposed algorithm exhibits robust performance and high effectiveness.