A Deep-Learning-Based Method for High-Precision Real-Time Detection of Steel Surface Defects

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.