Steel surface defect detection plays a vital role in industrial quality control, yet complex textures and multi-scale characteristics increase the difficulty of accurate detection. To address this, a lightweight detection network named MEA-YOLO, based on multi-scale edge enhancement and attention fusion, is proposed. The approach replaces the YOLOv11 backbone with StarNet to reduce computational complexity, introduces a multi-scale edge information enhancement module combined with a dual-domain selection mechanism to strengthen boundary and contextual features, and incorporates a spatially enhanced feedforward network in the detection head to improve fine-grained recognition. Experimental results show that the proposed method achieves an mAP50 of 74.58% on steel defect detection tasks, outperforming YOLOv11n by 2.01%, while reducing the number of parameters and GFLOPs by 14.3% and 10.1%, respectively. Additional evaluations on the GC10-DET dataset further demonstrate consistent improvements in both accuracy and inference speed, confirming the model′s robustness, generalization capability, and suitability for real-time industrial inspection.