Steel defects, stemming from issues like raw material imperfections and processing inconsistencies, present substantial challenges for the material’s effective use and subsequent manufacturing. Consequently, the real-time, accurate, and rapid detection of these defects is paramount in production, playing a vital role in cost reduction, efficiency enhancement, and resource conservation. To address these needs, this paper proposes a deep deep-learning-based image recognition method for defect detection using YOLOv7 (You Only Look Once), designated YOLOv7-SGS. This approach introduces a novel architecture, the YOLOv7-SGS network, which builds upon the standard YOLOv7. The enhancements include integrating a Shape-IoU model into the core backbone, innovatively incorporating an SGE attention mechanism, and refining the convolution algorithm with GSConv to boost model performance. The resulting YOLOv7-SGS model achieves an absolute 6% improvement in mAP@0.5 compared to the baseline model. Moreover, it attains a detection speed of 32 FPS, showcasing significant advantages and offering valuable insights for future research and practical applications.