Defect detection and multi-scale feature fusion of cold rolled strip based on lightweight YOLOv11 algorithm

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Introduction With the rapid and intelligent development of the steel manufacturing process, efficient and reliable detection of surface defects in cold-rolled strip steel is of great significance to product quality and production safety. However, existing detection methods have limited ability to identify small defects, and some high-precision models have problems such as large parameter scale and insufficient real-time performance. Methods This study proposes a cold-rolled strip defect detection and MSFF method based on lightweight YOLOv11. This method introduces an MSFF structure at the neck of the detection network and combines it with a SimAM-Enhanced Block to enhance the expression of key defect features. Results Experiments demonstrate that the designed method achieves an mAP of 57.85% in the cold-rolled strip defect detection task (IoU threshold 0.50-0.95), which is 4.67% and 11.61% higher than YOLOv8n’s 53.18% and YOLOv5s’ 46.24%. It significantly reduces the risk of missed detection while maintaining high detection accuracy, and the recall rate reaches 88.12%, which is significantly higher than YOLOv8n’s 83.24%, indicating that it has stronger detection capabilities for small and low-contrast defects. Discussion In summary, the proposed method achieves a good balance between detection accuracy, robustness, and engineering deployability, and can provide an efficient and practical solution for online defect detection of cold-rolled strip steel.

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