Enhanced YOLOv8s with Multi-Teacher Distillation for Steel Cord Ply Defect Detection

To improve detection accuracy for color-sensitive and small-target defects in steel cord ply, this paper introduces an improved YOLOv8s algorithm using multi-teacher stepwise hierarchical knowledge distillation for better adaptation across production lines. The improvements include: replacing the initial backbone convolutional layer with RGBV grouped convolution to enhance color feature extraction; substituting the SPPF module with SPPFCSPC-LSKA to improve multi-scale perception; and optimizing bounding box accuracy with the WIoU loss function. The multi-teacher distillation approach first transfers color feature learning using an RGBV-only teacher, then multi-scale feature learning with an SPPFCSPC-LSKA-only teacher. Experimental results show the improved model achieved 90.4% precision, 92.0% recall, 91.2% F1-score, and 97.2% mAP@0.5, surpassing the baseline YOLOv8s by 1.9, 2.2, 2.1, and 3.4 percentage points, respectively. The proposed model also achieves an inference time of 3.9 ms, representing a 1.0 ms reduction compared to the baseline. On a smaller dataset from another production line, single-teacher distillation increased precision, recall, F1-score, and mAP@0.5 to 84.6%, 82.0%, 83.3%, and 88.8%, respectively, albeit with an increase in inference time. The multi-teacher strategy further increased metrics to 97.5% precision, 88.8% recall, 92.9% F1-score, and 94.3% mAP@0.5, providing additional gains over single-teacher distillation while maintaining the same parameter count of 11.127 M and achieving a faster inference time of 4.1 ms on the target production line.