<p>Timely and accurate detection of steel surface defects is crucial for ensuring industrial product quality and production safety. However, existing methods often struggle to balance detection accuracy, inference speed, and model lightweightness, especially under complex industrial conditions with multi-scale defects. To address this issue, this work proposes EG-YOLO, a lightweight and efficient detection framework based on YOLO11n for real-time steel surface defect inspection. Specifically, the Efficient Multi-Scale Attention (EMA) module is embedded into the backbone to enhance multi-scale feature representation through cross-spatial learning, while Generalized Intersection over Union (GIoU) loss is adopted to improve bounding box regression and localization of small and irregular defects. Experiments on the NEU-DET dataset show that EG-YOLO achieves 79.7% mAP@0.5, 125.8 FPS, and only 2.53M parameters. Compared with the YOLO11n baseline, it improves mAP@0.5 by 5.4% with negligible speed overhead. Compared with other lightweight detectors, EG-YOLO gains 2.1% mAP@0.5 over YOLO13n at a similar model size and runs 1.4 times faster than RT-DETR-L while maintaining competitive accuracy. Cross-dataset evaluation on GC10-DET further demonstrates the generalization capability of EG-YOLO, indicating its potential for real-time industrial inspection.</p>