To address the issue of low recognition accuracy in lightweight algorithms for steel surface defect detection, this paper introduces a Multi-scale Enhanced Feature Fusion (EFF) technique. Initially, an Adaptive Weighted Fusion (AWF) module calculates fusion weights adaptively for different feature levels. This allows shallow features to enrich with deep semantics without compromising detail. Subsequently, the Spatial Feature Enhancement (SFE) module boosts the fused features from three distinct directions and improves network stability by integrating residual pathways, enabling the convolution process to extract more critical information. The model then selects better training samples based on the overlap between the prior box and the ground truth. Experimental outcomes show that the proposed method achieves a detection accuracy of 80.47%, marking a 6.81% increase over the baseline algorithm. Moreover, with 2.36 M parameters and 952.67 MFLOPs, this algorithm efficiently and accurately identifies steel surface defects, demonstrating significant practical utility.