Detection of surface defects in steel materials is a key link in ensuring the quality of products in the manufacturing industry. Manual visual inspection and basic optical detection methods suffer from low efficiency and high miss detection rates, and the limited samples of existing datasets restrict the model′s generalization ability. Therefore, this paper proposes a lightweight steel surface defect detection method that integrates LS-DCGAN data augmentation with an improved YOLOv8 model. Firstly, to address the issue of insufficient sample diversity in the NEU-DET dataset, we use an LS-DCGAN generative adversarial network for data augmentation, effectively supplementing the morphological features and distribution characteristics of defect samples. Secondly, we conduct triple optimization on the YOLOv8 model to propose the SPH-YOLO detection algorithm: reconstructing the C2f module structure to enhance feature extraction capabilities, embedding an attention mechanism to improve focus on defect areas, and designing a multi-level feature fusion pyramid for cross-scale information interaction. Finally, we validate the improved model on the enhanced NEU-DET and GC10-DET datasets. Experimental results show that the improved model achieves a 3.2% increase in mAP@50%, a 28.5% reduction in parameter count, and a 12.3% decrease in computational load. Furthermore, the improvement method exhibits strong generalization ability, effectively balancing the lightweight nature of the detection model and its detection performance.