A Study on Surface Defect Detection Algorithm of Strip Steel Based on YOLOv8n

Hot rolled steel strip has been extensively applied in industrial production and processing due to its outstanding properties.Nevertheless, during the production procedure, as a result of technological constraints, defects will inevitably occur on the surface of the steel strip, significantly influencing the performance and safety of the steel strip.Hence, how to detect the surface defects of steel strips has turned into the key point.In this paper, an enhanced YOLOv8n network model is proposed to make it applicable for the surface defect detection tasks of hot rolled steel strips.The improved model introduces Dynamic Snake Convolution and Efficient Multi-Scale Attention Module.The average precision of the improved model is 6.8 percentage points higher than that of the original model.