YOLOv8n-GSE: Efficient Steel Surface Defect Detection Method

At present, deep learning-based intelligent detection technologies are progressively being applied to the field of steel surface defect inspection. An efficient defect inspection algorithm, YOLOv8n-GSE, is proposed to overcome low detection accuracy. We integrate a Global Attention Mechanism (GAM) into the YOLOv8n-based model’s backbone network to construct GAM attention layers, specifically designed to enhance feature representation for steel surface defects. We implement a CSP-ABAN structure in both the backbone and neck networks to improve the YOLOv8n-GSE model’s capacity for extracting steel surface defect features, while the ESCD head is introduced to process multi-scale features and capture defect information across various dimensions. By replacing standard convolutions with GSConv, the model achieves a balance between complexity and detection efficiency. Furthermore, the PIoUv2 loss function is adopted to address the limitations of CIoU. The experimental results demonstrate that the YOLOv8n-GSE model reduces computational complexity by 31%, with its complexity reaching 5.6 GFLOPs compared with the baseline YOLOv8n model. Meanwhile, it achieves a mean average precision of 94.2%, which represents a 5.4 percentage point improvement. Compared with other object detection algorithms, YOLOv8n-GSE significantly enhances defect detection accuracy along with statistically significant reduction in miss rates.