DGYOLOv8: An Enhanced Model for Steel Surface Defect Detection Based on YOLOv8

The application of deep learning-based defect detection models significantly reduces the workload of workers and enhances the efficiency of inspections. In this paper, an enhanced YOLOv8 model (DCNv4_C2f + GAM + InnerMPDIoU + YOLOv8, hereafter referred to as DGYOLOv8) is developed to tackle the challenges of object detection in steel surface defect detection tasks. DGYOLOv8 incorporates a deformable convolution C2f (DCNv4_C2f) module into the backbone network to allow adaptive adjustment of the receptive field. Additionally, it integrates a Gate Attention Module (GAM) within the spatial and channel attention mechanisms, enhancing feature selection through a gating mechanism that strengthens key features, thereby improving the model's generalization and interpretability. The InnerMPDIoU, which incorporates the latest Inner concepts, enhances detection accuracy and the ability to handle detailed aspects effectively. This model helps to address the limitations of current networks. Experimental results show improvements in precision (P), recall (R), and mean average precision (mAP) compared to existing models.