An improved YOLO11 network integrated with sample generation for the intelligent detection of steel plate surface defects

To satisfy the stringent surface quality requirements imposed for industrial steel plates, automated defect detection techniques must balance high accuracy with low computational latency. In this paper, a lightweight detection method integrating adaptive image enhancement, generative sample synthesis, and an optimized detection model is proposed. First, an AW-ACE algorithm is introduced to resolve low contrast levels by dynamically merging color channels using information entropy. Second, an ES-DCGAN model based on ECA and an SELU is used to synthesize diverse and high-quality defect samples. Finally, we design a lightweight model, i.e., GE-YOLO11n, by incorporating GhostConv and ECA into YOLO11n to optimize the feature extraction process for small-scale defects. The entire framework yields a 1.5% mAP improvement and a 0.3 ms detector-only latency reduction relative to the baseline. Ablation studies demonstrate that the complete pipeline, comprising the ES-DCGAN, AW-ACE, and GE-YOLO11n modules, improves the overall mAP by 6.3% and reduces the number of required parameters by 21.4%. Compared with the evaluated models, our proposed method achieves an mAP value of 91.3% and a full-pipeline latency level of 7.4 ms, outperforming the other compared models under the experimental conditions specified in this study. This method delivers a highly efficient and robust solution for attaining real-time industrial quality control.