## 5 Experiment Results and Analysis
### Comparison Experiment with Different Models  
The ablation experiments demonstrated the feasibility of the proposed algorithm. To further validate its performance, the results of the improved model proposed in this paper were compared with those of six mainstream models, namely SSD, Faster R-CNN, DETR, and the YOLO series models, under the same dataset partition conditions. The experimental results can be seen in Table 3.  
As can be seen from the experimental results in Table 3, the mAP value of the DEW-YOLO model proposed in this paper improved by 8.4%, 4%, and 13.1% compared with SSD, Fast R-CNN, and DETR, respectively, with a significant improvement in accuracy. The SSD and Faster R-CNN detection models have slow detection speeds and are computationally  
Figure 11: Comparison of detection results. (**a**) Detection effect of YOLOv8n model. (**b**) Detection effect of DEW-YOLO model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type** & **SSD** & **Fast R-CNN** & **DETR** & **YOLOv5s** & **YOLOv7** & **YOLOv8n** & **DEW-YOLO** \\ \hline Crazing & 0.452 & 0.474 & 0.293 & 0.445 & 0.417 & 0.494 & 0.558 \\ Inclusion & 0.818 & 0.849 & 0.746 & 0.861 & 0.864 & 0.852 & 0.904 \\ Patches & 0.880 & 0.916 & 0.876 & 0.835 & 0.859 & 0.839 & 0.906 \\ Pitted surface & 0.842 & 0.851 & 0.814 & 0.852 & 0.825 & 0.85 & 0.853 \\ Rolled-in scale & 0.602 & 0.541 & 0.523 & 0.52 & 0.566 & 0.624 & 0.623 \\ Scratches & 0.722 & 0.947 & 0.925 & 0.906 & 0.887 & 0.926 & 0.973 \\ mAP (\%) & 71.9 & 76.3 & 67.2 & 73.7 & 73.6 & 76.4 & 80.3 \\ FPS & 69 & 19 & 12 & 108 & 38 & 132 & 91 \\ GFLOPs & 62.7 & 370.2 & 100.9 & 15.8 & 103.2 & 8.2 & 11.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of experimental results of different network models.  
intensive, and thus they do not redound to deployment and utilization on devices with limited computational resources. DETR is a Transformer-based target detection algorithm. When detecting multi-class steel defects, this model may encounter problems such as high memory consumption and slow convergence due to the global self-attention mechanism, which makes the DETR model less effective, and its mAP value was the lowest, being only 67.2%. In the case of diverse types and complex defects in steel, the detection accuracy of YOLOv5s and YOLOv7 was also unsatisfactory. Compared with YOLOv5s and YOLOv7, the mAP values of the DEW-YOLO model improved by 6.6% and 6.7%, respectively, resulting in a significant improvement in detection accuracy. And all six types of defect detection accuracies of the DEW-YOLO model were higher than YOLOv5s vs. YOLOv7. YOLOv8n was the fastest detection algorithm with a relatively small number of calculations, but its detection accuracy still needs to be improved. The DEW-YOLO algorithm achieved comprehensive improvements in detection performance. Although there was a slight decrease in detection speed, it still met the detection requirements in industrial applications, and the calculation count remained at a satisfactory level. In summary, the DEW-YOLO algorithm proposed in this paper outperformed the other algorithms in the task of detecting defects on steel surfaces, effectively recognized a wide range of defect classes, and met the terminal detection speed requirements.