To address the current challenges in metallic surface defect detection, such as large model parameters, insufficient precision, and deployment difficulties in industrial environments, an improved lightweight detection model based on YOLOv8 (you only look once version 8), named SDI-YOLO was proposed. The model integrated the spatial and channel reconst-ruction convolution module (SCconv), the C2f_Dual module, and the inverted residual and efficient multi-scale attention (iEMA) mechanism. These improvements effectively reduced the number of parameters and computational cost while enhanced detection accuracy. Experiments were conducted using SDI-YOLO on the NEU-DET and Magnetic-Tile-Defect datasets. The mAP50 values increased by 2.5% and 2.6%, respectively, compared with the YOLOv8. The number of parameters was reduced by 15.6%, and giga floating point operations per second was decreased by 10.9%.Compared with mainstream algorithms in the field, SDI-YOLO shows advantages in both detection accuracy and model size. It meets the requirements of edge device deployment, such as small parameter size and high recognition rate. SDI-YOLO provides an efficient and lightweight solution for metallic surface defect detection and has significant practical application value.