To address the issues of large parameter quantity, high computational complexity, and high resource demands on the computing platform for the steel surface defect detection model, a lightweight improved algorithm has been proposed. Firstly, using ShuffleNetV2 as the improved backbone layer has achieved remarkable results in reducing model complexity and computational load. Secondly, a sufficiently flexible and lightweight channel attention mechanism (CA) was incorporated after the SPPF module, while the bidirectional feature pyramid network (BiFPN) was utilized to enhance feature fusion, thereby improving the efficiency of feature information flow. Finally, the lightweight dual convolution kernel (DualConv) was employed to replace the convolution layer in C2f, and the parameter quantity was reduced through the grouping convolution strategy. Experimental results indicate that, compared with the original YOLOv8n, the improved model achieves lightweighting while maintaining detection accuracy. The parameter quantity is 56.2% of the original, and the volume and computational load have decreased to 3.6 MB and 4.8 GFLOPs, respectively, representing a reduction of 42.86% and 41.47% compared to the original model. The lightweighting of the model reduces the deployment cost and is suitable for practical deployment and application.